Search Results for author: Philip S. Yu

Found 284 papers, 113 papers with code

H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network

no code implementations EMNLP 2020 Shen Wang, Xiaokai Wei, Cicero Nogueira dos santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu

Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.

Graph Attention Knowledge Graph Embedding +2

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization

no code implementations31 Jan 2023 Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu

Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.

Node Classification

Provable Unrestricted Adversarial Training without Compromise with Generalizability

no code implementations22 Jan 2023 Lilin Zhang, Ning Yang, Yanchao Sun, Philip S. Yu

Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.

Adversarial Robustness

Self-organization Preserved Graph Structure Learning with Principle of Relevant Information

no code implementations30 Dec 2022 Qingyun Sun, JianXin Li, Beining Yang, Xingcheng Fu, Hao Peng, Philip S. Yu

Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships.

Graph structure learning

HUSP-SP: Faster Utility Mining on Sequence Data

1 code implementation29 Dec 2022 Chunkai Zhang, Yuting Yang, Zilin Du, Wensheng Gan, Philip S. Yu

High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.

Sequential Pattern Mining

Towards Sequence Utility Maximization under Utility Occupancy Measure

no code implementations20 Dec 2022 Gengsen Huang, Wensheng Gan, Philip S. Yu

An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.

Sequential Pattern Mining

Localized Contrastive Learning on Graphs

no code implementations8 Dec 2022 Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.

Contrastive Learning Data Augmentation +1

Learning to Select from Multiple Options

no code implementations1 Dec 2022 Jiangshu Du, Wenpeng Yin, Congying Xia, Philip S. Yu

To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.

Entity Typing Intent Detection +2

Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

no code implementations30 Nov 2022 Li Sun, Junda Ye, Hao Peng, Feiyang Wang, Philip S. Yu

On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.

Graph Learning

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

1 code implementation18 Nov 2022 Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.

Recommendation Systems

MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System

1 code implementation14 Nov 2022 Liangwei Yang, Shen Wang, Jibing Gong, Shaojie Zheng, Shuying Du, Zhiwei Liu, Philip S. Yu

To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).

Recommendation Systems

Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction

no code implementations11 Nov 2022 Xuming Hu, Shiao Meng, Chenwei Zhang, Xiangli Yang, Lijie Wen, Irwin King, Philip S. Yu

Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.

named-entity-recognition NER +3

Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph

1 code implementation2 Nov 2022 Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu

PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.

Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?

no code implementations28 Oct 2022 Byung-Hak Kim, Zhongfen Deng, Philip S. Yu, Varun Ganapathi

The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.

Knowledge Distillation Medical Code Prediction +1

Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer

1 code implementation24 Oct 2022 Ziwei Fan, Zhiwei Liu, Chen Wang, Peijie Huang, Hao Peng, Philip S. Yu

However, it remains a significant challenge to model auxiliary item relationships in SR. To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR).

Sequential Recommendation

EnTDA: Entity-to-Text based Data Augmentation Approach for Named Entity Recognition Tasks

no code implementations19 Oct 2022 Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

To alleviate the excessive reliance on the dependency order among entities in existing augmentation paradigms, we develop an entity-to-text instead of text-to-entity based data augmentation method named: EnTDA to decouple the dependencies between entities by adding, deleting, replacing and swapping entities, and adopt these augmented data to bootstrap the generalization ability of the NER model.

Data Augmentation named-entity-recognition +1

Variational Graph Generator for Multi-View Graph Clustering

no code implementations13 Oct 2022 Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He

The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.

Graph Clustering

Deep Clustering: A Comprehensive Survey

no code implementations9 Oct 2022 Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He

Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.

Deep Clustering

Totally-ordered Sequential Rules for Utility Maximization

no code implementations27 Sep 2022 Chunkai Zhang, Maohua Lyu, Wensheng Gan, Philip S. Yu

TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence.

Sequential Pattern Mining

Contrast Pattern Mining: A Survey

no code implementations27 Sep 2022 Yao Chen, Wensheng Gan, Yongdong Wu, Philip S. Yu

Contrast pattern mining (CPM) is an important and popular subfield of data mining.

Scene Graph Modification as Incremental Structure Expanding

no code implementations COLING 2022 Xuming Hu, Zhijiang Guo, Yu Fu, Lijie Wen, Philip S. Yu

A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

no code implementations4 Sep 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.

Link Prediction Network Embedding +1

A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning

no code implementations30 Aug 2022 Li Sun, Junda Ye, Hao Peng, Philip S. Yu

To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.

Graph Learning Graph Representation Learning +1

ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

1 code implementation27 Aug 2022 Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.

Contrastive Learning Sequential Recommendation

A Generic Algorithm for Top-K On-Shelf Utility Mining

no code implementations27 Aug 2022 Jiahui Chen, Xu Guo, Wensheng Gan, Shichen Wan, Philip S. Yu

Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications.

Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing

1 code implementation17 Aug 2022 Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu

Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.

Graph Learning Graph structure learning +1

Automating DBSCAN via Deep Reinforcement Learning

1 code implementation9 Aug 2022 Ruitong Zhang, Hao Peng, Yingtong Dou, Jia Wu, Qingyun Sun, Jingyi Zhang, Philip S. Yu

DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.

reinforcement-learning reinforcement Learning +1

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

1 code implementation21 Jun 2022 Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu

To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.

Anomaly Detection Graph Generation +1

Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

no code implementations15 Jun 2022 Yue Wang, Yao Wan, Lu Bai, Lixin Cui, Zhuo Xu, Ming Li, Philip S. Yu, Edwin R Hancock

To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.

Event Extraction Knowledge Graphs

Towards Target Sequential Rules

no code implementations9 Jun 2022 Wensheng Gan, Gengsen Huang, Jian Weng, Tianlong Gu, Philip S. Yu

In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining.

A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference

no code implementations31 May 2022 Shu'ang Li, Xuming Hu, Li Lin, Aiwei Liu, Lijie Wen, Philip S. Yu

Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).

Contrastive Learning Data Augmentation +4

Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory

no code implementations24 May 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, Philip S. Yu

To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.

Event Detection

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

1 code implementation NAACL 2022 Shuliang Liu, Xuming Hu, Chenwei Zhang, Shu`ang Li, Lijie Wen, Philip S. Yu

Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.

Contrastive Learning Relation Extraction

Multifaceted Improvements for Conversational Open-Domain Question Answering

no code implementations1 Apr 2022 TingTing Liang, Yixuan Jiang, Congying Xia, Ziqiang Zhao, Yuyu Yin, Philip S. Yu

Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.

Open-Domain Question Answering Retrieval

Improving Contrastive Learning with Model Augmentation

1 code implementation25 Mar 2022 Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong

However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.

Contrastive Learning Data Augmentation +2

Deep reinforcement learning guided graph neural networks for brain network analysis

no code implementations18 Mar 2022 Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning reinforcement Learning +1

Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shots

1 code implementation Findings (EMNLP) 2021 Wenting Zhao, Ye Liu, Yao Wan, Philip S. Yu

Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data.

Table-to-Text Generation

TaSPM: Targeted Sequential Pattern Mining

no code implementations26 Feb 2022 Gengsen Huang, Wensheng Gan, Philip S. Yu

What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes.

Sequential Pattern Mining

Towards Revenue Maximization with Popular and Profitable Products

no code implementations26 Feb 2022 Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier-Viger, Chien-Ming Chen, Philip S. Yu

To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.

Marketing

Graph Masked Autoencoders with Transformers

1 code implementation17 Feb 2022 Sixiao Zhang, Hongxu Chen, Haoran Yang, Xiangguo Sun, Philip S. Yu, Guandong Xu

In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.

Graph Classification Node Classification

Graph Neural Networks for Graphs with Heterophily: A Survey

no code implementations14 Feb 2022 Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, Philip S. Yu

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications.

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

1 code implementation7 Feb 2022 Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan, Philip S. Yu

We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.

Recommendation Systems

Link Prediction with Contextualized Self-Supervision

no code implementations25 Jan 2022 Daokun Zhang, Jie Yin, Philip S. Yu

To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance.

Inductive Link Prediction Self-Supervised Learning

Dual Space Graph Contrastive Learning

no code implementations19 Jan 2022 Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, Guandong Xu

In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.

Contrastive Learning Graph Learning +1

Sequential Recommendation via Stochastic Self-Attention

1 code implementation16 Jan 2022 Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu

We further argue that BPR loss has no constraint on positive and sampled negative items, which misleads the optimization.

Sequential Recommendation

Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

no code implementations16 Jan 2022 Ziwen Du, Ning Yang, Zhonghua Yu, Philip S. Yu

To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.

reinforcement-learning reinforcement Learning

Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning

no code implementations16 Jan 2022 Xiaoyun Zhao, Ning Yang, Philip S. Yu

Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.

Domain Adaptation Recommendation Systems

Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection

no code implementations15 Jan 2022 Yuefei Lyu, Xiaoyu Yang, Jiaxin Liu, Philip S. Yu, Sihong Xie, Xi Zhang

To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors.

reinforcement-learning reinforcement Learning

Graph Structure Learning with Variational Information Bottleneck

1 code implementation16 Dec 2021 Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.

Graph structure learning

A Self-supervised Mixed-curvature Graph Neural Network

no code implementations10 Dec 2021 Li Sun, Zhongbao Zhang, Junda Ye, Hao Peng, Jiawei Zhang, Sen Su, Philip S. Yu

Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.

Contrastive Learning Graph Representation Learning

US-Rule: Discovering Utility-driven Sequential Rules

no code implementations29 Nov 2021 Gengsen Huang, Wensheng Gan, Jian Weng, Philip S. Yu

High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining.

Sequential Pattern Mining

Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network

no code implementations28 Nov 2021 Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip S. Yu, Kannan Achan

In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.

Recommendation Systems

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

no code implementations25 Nov 2021 Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Zonghan Wu, Cheng Wang, Philip S. Yu

In such a graph, the correlations between different nodes at different time steps are not explicitly reflected, which may restrict the learning ability of graph neural networks.

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

no code implementations24 Nov 2021 Yicong Li, Hongxu Chen, Yile Li, Lin Li, Philip S. Yu, Guandong Xu

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.

Explainable Recommendation Knowledge Graphs +3

Federated Social Recommendation with Graph Neural Network

no code implementations21 Nov 2021 Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, Philip S. Yu

However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.

Federated Learning Recommendation Systems

Pre-training Graph Neural Network for Cross Domain Recommendation

no code implementations16 Nov 2021 Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, Philip S. Yu

Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.

Graph Representation Learning Recommendation Systems

CvS: Classification via Segmentation For Small Datasets

no code implementations29 Oct 2021 Nooshin Mojab, Philip S. Yu, Joelle A. Hallak, Darvin Yi

The success of deep learning methods relies heavily on the availability of a large amount of data.

Classification

Dense Hierarchical Retrieval for Open-Domain Question Answering

1 code implementation Findings (EMNLP) 2021 Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip S. Yu

In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.

Open-Domain Question Answering Retrieval +1

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning

no code implementations14 Oct 2021 Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long

While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.

Few-Shot Learning Transfer Learning

Deep Fraud Detection on Non-attributed Graph

no code implementations4 Oct 2021 Chen Wang, Yingtong Dou, Min Chen, Jia Chen, Zhiwei Liu, Philip S. Yu

The successes of most previous methods heavily rely on rich node features and high-fidelity labels.

Contrastive Learning Fraud Detection

Self-supervised Learning for Sequential Recommendation with Model Augmentation

no code implementations29 Sep 2021 Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong

However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.

Contrastive Learning Data Augmentation +2

ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning

no code implementations29 Sep 2021 Hengrui Zhang, Qitian Wu, Shaofeng Zhang, Junchi Yan, David Wipf, Philip S. Yu

In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.

Contrastive Learning Representation Learning +1

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

1 code implementation EMNLP 2021 Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.

Meta-Learning Pseudo Label +4

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

no code implementations7 Sep 2021 Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, Guandong Xu

They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.

Contrastive Learning Multi-Task Learning +1

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

1 code implementation26 Aug 2021 Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.

Knowledge Graphs Recommendation Systems

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

1 code implementation14 Aug 2021 Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, Philip S. Yu

Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.

Sequential Recommendation

Contrastive Self-supervised Sequential Recommendation with Robust Augmentation

1 code implementation14 Aug 2021 Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong

In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.

Contrastive Learning Self-Supervised Learning +1

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Dual Adversarial Variational Embedding for Robust Recommendation

no code implementations30 Jun 2021 Qiaomin Yi, Ning Yang, Philip S. Yu

First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.

Variational Inference

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +2

Sketch-Based Anomaly Detection in Streaming Graphs

1 code implementation8 Jun 2021 Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi

This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).

Anomaly Detection Intrusion Detection

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Graph Learning based Recommender Systems: A Review

1 code implementation13 May 2021 Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).

Collaborative Filtering Graph Learning +1

User Preference-aware Fake News Detection

1 code implementation25 Apr 2021 Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

Fact Checking Fake News Detection +2

Membership Inference Attacks on Knowledge Graphs

no code implementations16 Apr 2021 Yu Wang, Lifu Huang, Philip S. Yu, Lichao Sun

Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.

Inference Attack Knowledge Graph Embedding +3

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

1 code implementation16 Apr 2021 Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu

To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.

Fraud Detection Navigate +2

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

1 code implementation NAACL 2021 Zhongfen Deng, Hao Peng, Dongxiao He, JianXin Li, Philip S. Yu

The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.

General Classification Representation Learning +2

Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

no code implementations6 Apr 2021 Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu

To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Event Detection

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications

no code implementations30 Mar 2021 Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi, Philip S. Yu, Joelle A. Hallak

In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.

An Introduction to Robust Graph Convolutional Networks

no code implementations27 Mar 2021 Mehrnaz Najafi, Philip S. Yu

In this paper, we propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data where data may come from multiple sources.

Word Embeddings

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

2 code implementations17 Mar 2021 Yunbo Wang, Haixu Wu, Jianjin Zhang, Zhifeng Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long

This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Video Prediction Weather Forecasting

Membership Inference Attacks on Machine Learning: A Survey

2 code implementations14 Mar 2021 Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S. Yu, Xuyun Zhang

In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.

BIG-bench Machine Learning Fairness +4

Understanding WeChat User Preferences and "Wow" Diffusion

1 code implementation4 Mar 2021 Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu

"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.

Graph Representation Learning Social and Information Networks

Generalizing to Unseen Domains: A Survey on Domain Generalization

1 code implementation2 Mar 2021 Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, Philip S. Yu

Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.

Domain Generalization Out-of-Distribution Generalization +1

Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation

no code implementations22 Jan 2021 Ye Liu, Yao Wan, Jian-Guo Zhang, Wenting Zhao, Philip S. Yu

In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.

Machine Translation Translation

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +4

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.

reinforcement Learning

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.

Management

Heterogeneous Similarity Graph Neural Network on Electronic Health Records

no code implementations17 Jan 2021 Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu

EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.

Dynamic Graph Collaborative Filtering

1 code implementation8 Jan 2021 Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, Philip S. Yu

Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.

Collaborative Filtering Recommendation Systems

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

no code implementations3 Jan 2021 Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang

We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Community Detection

Privacy and Robustness in Federated Learning: Attacks and Defenses

no code implementations7 Dec 2020 Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.

Federated Learning Privacy Preserving

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

no code implementations30 Nov 2020 Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.

Graph Classification Graph Embedding +4

Deoscillated Graph Collaborative Filtering

1 code implementation4 Nov 2020 Zhiwei Liu, Lin Meng, Fei Jiang, Jiawei Zhang, Philip S. Yu

Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.

Collaborative Filtering Recommendation Systems

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

1 code implementation COLING 2020 Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.

Decision Making

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

2 code implementations COLING 2020 Hu Xu, Lei Shu, Philip S. Yu, Bing Liu

Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context.

Aspect-Based Sentiment Analysis Language Modelling +1

Basket Recommendation with Multi-Intent Translation Graph Neural Network

1 code implementation22 Oct 2020 Zhiwei Liu, Xiaohan Li, Ziwei Fan, Stephen Guo, Kannan Achan, Philip S. Yu

The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.

Translation

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

no code implementations13 Oct 2020 Yue Wang, Zhuo Xu, Lu Bai, Yao Wan, Lixin Cui, Qian Zhao, Edwin R. Hancock, Philip S. Yu

To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.

Event Extraction TAG

Semi-supervised Relation Extraction via Incremental Meta Self-Training

1 code implementation Findings (EMNLP) 2021 Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu

To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.

Meta-Learning Pseudo Label +1

Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

1 code implementation Findings of the Association for Computational Linguistics 2020 Hoang Nguyen, Chenwei Zhang, Congying Xia, Philip S. Yu

Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components.

Few-Shot Learning Generalized Few-Shot Learning +1

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score

1 code implementation28 Sep 2020 He Huang, Shunta Saito, Yuta Kikuchi, Eiichi Matsumoto, Wei Tang, Philip S. Yu

Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing.

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

1 code implementation26 Sep 2020 Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.

Graph Attention Text Generation

Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

no code implementations25 Sep 2020 Tao Zhang, Tianqing Zhu, Jing Li, Mengde Han, Wanlei Zhou, Philip S. Yu

A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.

BIG-bench Machine Learning Ensemble Learning +1

Fairness Constraints in Semi-supervised Learning

no code implementations14 Sep 2020 Tao Zhang, Tianqing Zhu, Mengde Han, Jing Li, Wanlei Zhou, Philip S. Yu

Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.

BIG-bench Machine Learning Fairness

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency

2 code implementations24 Aug 2020 Youwei Liang, Dong Huang, Chang-Dong Wang, Philip S. Yu

To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned.

Graph Learning

Differentially Private Multi-Agent Planning for Logistic-like Problems

no code implementations16 Aug 2020 Dayong Ye, Tianqing Zhu, Sheng Shen, Wanlei Zhou, Philip S. Yu

To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning as a means of preserving the privacy of agents for logistic-like problems.

Privacy Preserving

Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

2 code implementations12 Aug 2020 Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.

Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering

no code implementations6 Aug 2020 Ye Liu, Shaika Chowdhury, Chenwei Zhang, Cornelia Caragea, Philip S. Yu

Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge.

Multiple-choice Question Answering

Data science and AI in FinTech: An overview

no code implementations10 Jul 2020 Longbing Cao, Qiang Yang, Philip S. Yu

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas.

BIG-bench Machine Learning Federated Learning +1

Network Embedding with Completely-imbalanced Labels

2 code implementations IEEE Transactions on Knowledge and Data Engineering 2020 Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, Philip S. Yu

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.

Network Embedding

GCN for HIN via Implicit Utilization of Attention and Meta-paths

no code implementations6 Jul 2020 Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han

Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

no code implementations4 Jul 2020 Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.

Virology

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations23 Jun 2020 Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

Representation Learning

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Robust Spammer Detection by Nash Reinforcement Learning

1 code implementation10 Jun 2020 Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie

We experiment on three large review datasets using various state-of-the-art spamming and detection strategies and show that the optimization algorithm can reliably find an equilibrial detector that can robustly and effectively prevent spammers with any mixed spamming strategies from attaining their practical goal.

Fraud Detection reinforcement-learning +1

User Memory Reasoning for Conversational Recommendation

no code implementations COLING 2020 Hu Xu, Seungwhan Moon, Honglei Liu, Pararth Shah, Bing Liu, Philip S. Yu

We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.

Joint Training Capsule Network for Cold Start Recommendation

no code implementations23 May 2020 Ting-Ting Liang, Congying Xia, Yuyu Yin, Philip S. Yu

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.

Deep Learning for Community Detection: Progress, Challenges and Opportunities

1 code implementation17 May 2020 Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.

Community Detection Graph Embedding

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

1 code implementation1 May 2020 Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng

In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.

Fraud Detection

DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis

1 code implementation Findings of the Association for Computational Linguistics 2020 Hu Xu, Bing Liu, Lei Shu, Philip S. Yu

This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding.

Aspect-Based Sentiment Analysis Language Modelling

Differentially Private Deep Learning with Smooth Sensitivity

no code implementations1 Mar 2020 Lichao Sun, Yingbo Zhou, Philip S. Yu, Caiming Xiong

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.

Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping

1 code implementation11 Feb 2020 Shao-Heng Ko, Hsu-Chao Lai, Hong-Han Shuai, De-Nian Yang, Wang-Chien Lee, Philip S. Yu

Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user.

Data Structures and Algorithms

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

1 code implementation2 Feb 2020 Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

Knowledge Graph Embedding Relational Reasoning

Deep Collaborative Embedding for information cascade prediction

no code implementations18 Jan 2020 Yuhui Zhao, Ning Yang, Tao Lin, Philip S. Yu

First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion.

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

1 code implementation18 Jan 2020 Huanrui Luo, Ning Yang, Philip S. Yu

Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item.

Explainable Recommendation

BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network

1 code implementation14 Jan 2020 Zhiwei Liu, Mengting Wan, Stephen Guo, Kannan Achan, Philip S. Yu

By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.

Collaborative Filtering Link Prediction

Deep Graph Similarity Learning: A Survey

no code implementations25 Dec 2019 Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke, Philip S. Yu

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search.

Graph Similarity

VideoDG: Generalizing Temporal Relations in Videos to Novel Domains

1 code implementation8 Dec 2019 Zhiyu Yao, Yunbo Wang, Jianmin Wang, Philip S. Yu, Mingsheng Long

This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions.

Action Recognition Data Augmentation +3

Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings

no code implementations6 Dec 2019 Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

Distributed representations of medical concepts have been used to support downstream clinical tasks recently.