Search Results for author: Philip S. Yu

Found 400 papers, 167 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

GC-Bench: An Open and Unified Benchmark for Graph Condensation

1 code implementation30 Jun 2024 Qingyun Sun, Ziying Chen, Beining Yang, Cheng Ji, Xingcheng Fu, Sheng Zhou, Hao Peng, JianXin Li, Philip S. Yu

To fill this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to analyze the performance of graph condensation in different scenarios systematically.

Large Language Models for Link Stealing Attacks Against Graph Neural Networks

no code implementations22 Jun 2024 Faqian Guan, Tianqing Zhu, Hui Sun, Wanlei Zhou, Philip S. Yu

The handling of these varying data dimensions posed a challenge in using a single model to effectively conduct link stealing attacks on different datasets.

Recommendation Systems

Taxonomy-Guided Zero-Shot Recommendations with LLMs

1 code implementation20 Jun 2024 Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, Kai Shu

With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise.

Recommendation Systems

Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation

no code implementations18 Jun 2024 Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu

However, training data cannot be accessed on the server under the federated learning paradigm, conflicting with the requirements of the centralized unlearning process.

Federated Learning Machine Unlearning +1

A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models

no code implementations17 Jun 2024 Haopeng Zhang, Philip S. Yu, Jiawei Zhang

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs).

Benchmarking Text Summarization

Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives

no code implementations16 Jun 2024 LinLin Wang, Tianqing Zhu, Wanlei Zhou, Philip S. Yu

Building upon our observations, we identify the trade-offs between privacy and fairness and between security and fairness within the context of federated learning.

Fairness Federated Learning

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions

no code implementations16 Jun 2024 Laiqiao Qin, Tianqing Zhu, Wanlei Zhou, Philip S. Yu

We discuss how KD can address the challenges in FL, including privacy protection, data heterogeneity, communication efficiency, and personalization.

Federated Learning Knowledge Distillation +3

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning

1 code implementation14 Jun 2024 Jiawen Qin, Haonan Yuan, Qingyun Sun, Lyujin Xu, Jiaqi Yuan, Pengfeng Huang, Zhaonan Wang, Xingcheng Fu, Hao Peng, JianXin Li, Philip S. Yu

Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains.

Graph Learning

PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection

1 code implementation4 Jun 2024 Ronghui Xu, Hao Miao, Senzhang Wang, Philip S. Yu, Jianxin Wang

To bridge the gap between the decentralized time series data and the centralized anomaly detection algorithms, we propose a Parameter-efficient Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns.

Federated Learning Large Language Model +1

Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement

1 code implementation3 Jun 2024 Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu

Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection.

Decision Making Disentanglement +5

Graph Neural Networks for Brain Graph Learning: A Survey

no code implementations1 Jun 2024 Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David Mcalpine, Paul Sowman, Alexis Giral, Philip S. Yu

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders.

Graph Learning

Unleashing the Potential of Diffusion Models for Incomplete Data Imputation

1 code implementation31 May 2024 Hengrui Zhang, Liancheng Fang, Philip S. Yu

This paper introduces DiffPuter, an iterative method for missing data imputation that leverages the Expectation-Maximization (EM) algorithm and Diffusion Models.

Imputation

Large Language Models Meet NLP: A Survey

1 code implementation21 May 2024 Libo Qin, Qiguang Chen, Xiachong Feng, Yang Wu, Yongheng Zhang, Yinghui Li, Min Li, Wanxiang Che, Philip S. Yu

While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored.

LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering

no code implementations20 May 2024 Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang, Chunyang Liu, Philip S. Yu

DSI is also theoretically presented as a new graph clustering objective, not requiring the predefined cluster number.

Clustering Deep Clustering +1

Large Language Models for Medicine: A Survey

no code implementations20 May 2024 Yanxin Zheng, Wensheng Gan, Zefeng Chen, Zhenlian Qi, Qian Liang, Philip S. Yu

To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed.

Large Language Models for Education: A Survey

no code implementations12 May 2024 Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, Philip S. Yu

In this paper, we conduct a systematic review of LLMEdu, focusing on current technologies, challenges, and future developments.

Autonomous Driving speech-recognition +1

Mixed Supervised Graph Contrastive Learning for Recommendation

no code implementations24 Apr 2024 Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu

Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.

Collaborative Filtering Contrastive Learning +2

Relational Prompt-based Pre-trained Language Models for Social Event Detection

no code implementations12 Apr 2024 Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu

In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection).

Event Detection Graph Neural Network

Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers

no code implementations7 Apr 2024 Libo Qin, Qiguang Chen, YuHang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S. Yu

To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.

Language Modelling Large Language Model

Instruction-based Hypergraph Pretraining

no code implementations28 Mar 2024 Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu

However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.

Diversity Graph Learning +2

Large Language Models for Education: A Survey and Outlook

no code implementations26 Mar 2024 Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen

The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.

Uncertainty in Graph Neural Networks: A Survey

no code implementations11 Mar 2024 Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu

Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.

Graph Learning

A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

no code implementations7 Mar 2024 Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang

To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.

Fraud Detection

Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions

no code implementations25 Feb 2024 Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo

To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.

Retrieval

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks

no code implementations24 Feb 2024 Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma

The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.

Contrastive Learning Graph Learning +1

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

no code implementations18 Feb 2024 Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.

Grammatical Error Correction

Disclosure and Mitigation of Gender Bias in LLMs

1 code implementation17 Feb 2024 Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee

Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.

When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

1 code implementation16 Feb 2024 Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma, Yangning Li, Hai-Tao Zheng, Xuming Hu, Philip S. Yu

In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp.

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction

no code implementations14 Feb 2024 Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu

Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.

Conformal Prediction Model Selection +1

Multitask Active Learning for Graph Anomaly Detection

1 code implementation24 Jan 2024 Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu

Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies.

Active Learning Graph Anomaly Detection +2

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

no code implementations23 Jan 2024 Li Sun, Zhenhao Huang, Hua Wu, Junda Ye, Hao Peng, Zhengtao Yu, Philip S. Yu

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph.

Contrastive Learning Graph structure learning

Cyclic Neural Network

no code implementations11 Jan 2024 Liangwei Yang, Hengrui Zhang, Zihe Song, Jiawei Zhang, Weizhi Zhang, Jing Ma, Philip S. Yu

This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.

Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces

no code implementations2 Jan 2024 Li Sun, Junda Ye, Jiawei Zhang, Yong Yang, Mingsheng Liu, Feiyang Wang, Philip S. Yu

To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.

Contrastive Learning Recommendation Systems

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

no code implementations30 Dec 2023 Yao Wan, Yang He, Zhangqian Bi, JianGuo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu

We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.

Representation Learning

Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models

1 code implementation29 Dec 2023 Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu

One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.

Data Augmentation Denoising +1

Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation

no code implementations26 Dec 2023 Zi-Feng Mai, Chang-Dong Wang, Zhongjie Zeng, Ya Li, Jiaquan Chen, Philip S. Yu

To settle the above challenges, we propose a novel method HEKP4NBR, which transforms the knowledge graph (KG) into prompts, namely Knowledge Tree Prompt (KTP), to help PLM encode the OOV item IDs in the user's basket sequence.

Next-basket recommendation

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

1 code implementation19 Dec 2023 Yuwei Cao, Hao Peng, Zhengtao Yu, Philip S. Yu

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance.

Event Detection Graph Neural Network

ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton

1 code implementation18 Dec 2023 Wei-Yao Wang, Wen-Chih Peng, Wei Wang, Philip S. Yu

Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e. g., pedestrian predictions and marketing bidding.

Decision Making Marketing

DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation

no code implementations18 Dec 2023 Yu Wang, Zhiwei Liu, JianGuo Zhang, Weiran Yao, Shelby Heinecke, Philip S. Yu

With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.

In-Context Learning Sequential Recommendation

Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction

1 code implementation18 Dec 2023 Kun Peng, Lei Jiang, Hao Peng, Rui Liu, Zhengtao Yu, Jiaqian Ren, Zhifeng Hao, Philip S. Yu

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.

Aspect Sentiment Triplet Extraction

kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning

no code implementations17 Dec 2023 Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip S. Yu

Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.

In-Context Learning Prompt Engineering +1

A Survey of Text Watermarking in the Era of Large Language Models

no code implementations13 Dec 2023 Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li, Xuming Hu, Xi Zhang, Lijie Wen, Irwin King, Hui Xiong, Philip S. Yu

Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.

Dialogue Generation

Large Language Models in Law: A Survey

no code implementations26 Nov 2023 Jinqi Lai, Wensheng Gan, Jiayang Wu, Zhenlian Qi, Philip S. Yu

With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry.

Text Generation

Multimodal Large Language Models: A Survey

no code implementations22 Nov 2023 Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, Philip S. Yu

By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.

Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering

no code implementations21 Nov 2023 Ke Xu, Yuanjie Zhu, Weizhi Zhang, Philip S. Yu

This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers.

Collaborative Filtering

Multi-view Graph Convolution for Participant Recommendation

no code implementations20 Nov 2023 Xiaolong Liu, Liangwei Yang, Chen Wang, Mingdai Yang, Zhiwei Liu, Philip S. Yu

Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.

Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation

1 code implementation16 Nov 2023 Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Xiaohan Li, Mingdai Yang, Chen Wang, Philip S. Yu

The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.

Informativeness Recommendation Systems

DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models

no code implementations14 Nov 2023 Yibo Wang, Xiangjue Dong, James Caverlee, Philip S. Yu

DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.

Adversarial Attack

Model-as-a-Service (MaaS): A Survey

no code implementations10 Nov 2023 Wensheng Gan, Shicheng Wan, Philip S. Yu

MaaS is a new deployment and service paradigm for different AI-based models.

Cloud Computing Language Modelling +1

JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction

1 code implementation7 Nov 2023 Zhongfen Deng, Hao Peng, Tao Zhang, Shuaiqi Liu, Wenting Zhao, Yibo Wang, Philip S. Yu

Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text.

Attribute Attribute Value Extraction +4

Joint Learning of Local and Global Features for Aspect-based Sentiment Classification

no code implementations2 Nov 2023 Hao Niu, Yun Xiong, Xiaosu Wang, Philip S. Yu

Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively.

Graph Attention Representation Learning +4

Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation

no code implementations1 Nov 2023 Jiangnan Xia, Yu Yang, Senzhang Wang, Hongzhi Yin, Jiannong Cao, Philip S. Yu

To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.

Data Augmentation Representation Learning

DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text

no code implementations31 Oct 2023 Wenting Zhao, Ye Liu, Tong Niu, Yao Wan, Philip S. Yu, Shafiq Joty, Yingbo Zhou, Semih Yavuz

Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e. g., knowledge base and text).

Knowledge Graphs Open-Domain Question Answering +2

Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

1 code implementation30 Oct 2023 Jiaqian Ren, Hao Peng, Lei Jiang, Zhiwei Liu, Jia Wu, Zhengtao Yu, Philip S. Yu

While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.

Contrastive Learning Event Detection

Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction

1 code implementation25 Oct 2023 Xuming Hu, Junzhe Chen, Aiwei Liu, Shiao Meng, Lijie Wen, Philip S. Yu

Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.

Relation Relation Extraction

CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks

1 code implementation23 Oct 2023 Hoang H. Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang, Philip S. Yu

While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.

Abstract Meaning Representation Natural Language Understanding

Knowledge Graph Context-Enhanced Diversified Recommendation

1 code implementation20 Oct 2023 Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu

Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.

Diversity Knowledge Graphs +1

Towards Graph Foundation Models: A Survey and Beyond

no code implementations18 Oct 2023 Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi

Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.

Graph Learning

Collaborative Semantic Alignment in Recommendation Systems

no code implementations13 Oct 2023 Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu

However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.

Collaborative Filtering Language Modelling +1

AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction

1 code implementation11 Oct 2023 Zhongfen Deng, Wei-Te Chen, Lei Chen, Philip S. Yu

In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).

Attribute Attribute Value Extraction +2

Do Large Language Models Know about Facts?

no code implementations8 Oct 2023 Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, Zhijiang Guo

Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.

Question Answering Text Generation

Graph Neural Architecture Search with GPT-4

no code implementations30 Sep 2023 Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, Jiajun Bu, Philip S. Yu

In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).

Neural Architecture Search

Discovering Utility-driven Interval Rules

1 code implementation28 Sep 2023 Chunkai Zhang, Maohua Lyu, Huaijin Hao, Wensheng Gan, Philip S. Yu

For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences.

Relation

Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

1 code implementation20 Sep 2023 Yibo Wang, Wenting Zhao, Yao Wan, Zhongfen Deng, Philip S. Yu

In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.

Machine Reading Comprehension Multi-Task Learning +3

All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm

no code implementations7 Sep 2023 Jiangshu Du, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu

In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.

Domain Generalization Intent Detection

Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

1 code implementation5 Sep 2023 Guangjie Zeng, Hao Peng, Angsheng Li, Zhiwei Liu, Chunyang Liu, Philip S. Yu, Lifang He

In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.

Lesion Segmentation Outlier Detection +2

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

1 code implementation31 Aug 2023 Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr

Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.

Privacy Preserving

LLMRec: Benchmarking Large Language Models on Recommendation Task

1 code implementation23 Aug 2023 Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu

The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.

Benchmarking Explanation Generation +1

Graph-based Alignment and Uniformity for Recommendation

1 code implementation18 Aug 2023 Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu

To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.

Collaborative Filtering Recommendation Systems +1

Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning

1 code implementation9 Aug 2023 Hoang H. Nguyen, Chenwei Zhang, Ye Liu, Philip S. Yu

Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.

Contrastive Learning Intent Detection +5

An Unforgeable Publicly Verifiable Watermark for Large Language Models

3 code implementations30 Jul 2023 Aiwei Liu, Leyi Pan, Xuming Hu, Shu'ang Li, Lijie Wen, Irwin King, Philip S. Yu

Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks.

Computational Efficiency

Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

no code implementations29 Jul 2023 Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu

Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.

Position

Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

1 code implementation10 Jul 2023 Hoang H. Nguyen, Chenwei Zhang, Tao Zhang, Eugene Rohrbaugh, Philip S. Yu

Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.

Cross-Lingual Transfer named-entity-recognition +3

A Survey on Evaluation of Large Language Models

1 code implementation6 Jul 2023 Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.

Ethics

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

1 code implementation28 Jun 2023 Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu

To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.

Collaborative Filtering

Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation

1 code implementation26 Jun 2023 Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu

We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation.

Attribute Multi-Task Learning +1

Sequential Recommendation with Controllable Diversification: Representation Degeneration and Diversity

1 code implementation21 Jun 2023 Ziwei Fan, Zhiwei Liu, Hao Peng, Philip S. Yu

We then propose a novel Singular sPectrum sMoothing regularization for Recommendation (SPMRec), which acts as a controllable surrogate to alleviate the degeneration and achieve the balance between recommendation diversity and performance.

Diversity Sequential Recommendation

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

1 code implementation8 Jun 2023 Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).

Drug Discovery Graph Learning +2

Machine Unlearning: A Survey

no code implementations6 Jun 2023 Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.

Machine Unlearning Medical Diagnosis +2

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

1 code implementation3 Jun 2023 Mengzhu Sun, Xi Zhang, Jianqiang Ma, Sihong Xie, Yazheng Liu, Philip S. Yu

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers.

Representation Learning

Decentralized Federated Learning: A Survey and Perspective

no code implementations2 Jun 2023 Liangqi Yuan, Ziran Wang, Lichao Sun, Philip S. Yu, Christopher G. Brinton

Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.

Federated Learning

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

no code implementations26 May 2023 Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu

These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.

Data Augmentation Relation +1

Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis

no code implementations25 May 2023 Xuming Hu, Zhijiang Guo, Zhiyang Teng, Irwin King, Philip S. Yu

Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.

Cross-Modal Retrieval Object +4

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

no code implementations21 May 2023 Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu

To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.

Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training

no code implementations12 May 2023 Ziwei Fan, Zhiwei Liu, Shelby Heinecke, JianGuo Zhang, Huan Wang, Caiming Xiong, Philip S. Yu

This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.

Recommendation Systems

Contrastive Graph Clustering in Curvature Spaces

no code implementations5 May 2023 Li Sun, Feiyang Wang, Junda Ye, Hao Peng, Philip S. Yu

On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.

Clustering Contrastive Learning +1

Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence

1 code implementation2 May 2023 Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu

In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i. e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy.

Claim Verification Decision Making +1

Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

1 code implementation2 May 2023 Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.

counterfactual Relation +2

Hierarchical State Abstraction Based on Structural Information Principles

1 code implementation24 Apr 2023 Xianghua Zeng, Hao Peng, Angsheng Li, Chunyang Liu, Lifang He, Philip S. Yu

State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.

Continuous Control Decision Making +1

Conditional Denoising Diffusion for Sequential Recommendation

no code implementations22 Apr 2023 Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.

Decoder Denoising +1

Graph Collaborative Signals Denoising and Augmentation for Recommendation

1 code implementation6 Apr 2023 Ziwei Fan, Ke Xu, Zhang Dong, Hao Peng, Jiawei Zhang, Philip S. Yu

Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions.

Collaborative Filtering Denoising +1

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

1 code implementation7 Mar 2023 Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun

The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

no code implementations13 Feb 2023 Song Wu, Yazhou Ren, Aodi Yang, Xinyue Chen, Xiaorong Pu, Jing He, Liqiang Nie, Philip S. Yu

In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.

COVID-19 Diagnosis Image Classification

Weakly Supervised Anomaly Detection: A Survey

2 code implementations9 Feb 2023 Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, Yue Zhao

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.

Supervised Anomaly Detection Time Series +2

ConsRec: Learning Consensus Behind Interactions for Group Recommendation

1 code implementation7 Feb 2023 Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu

Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.

MULTI-VIEW LEARNING

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

1 code implementation1 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.

Diversity Graph Neural Network +1

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

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

Entity-to-Text based Data Augmentation for various 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

Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).

Data Augmentation Diversity +4

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.

Clustering 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.

Clustering Deep Clustering

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.

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

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

1 code implementation4 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.

Attribute Link Prediction +2

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 Neural Network +2

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 +2

Automating DBSCAN via Deep Reinforcement Learning

2 code implementations9 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.

Clustering Computational Efficiency +3

BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

2 code implementations21 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 Benchmarking +2

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 +5

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 Graph Neural Network

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

1 code implementation NAACL 2022 Xuming Hu, Shuliang Liu, 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.

Clustering Contrastive Learning +3

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