Search Results for author: Shirui Pan

Found 101 papers, 45 papers with code

Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games

1 code implementation ACL 2022 Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf

Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.

Efficient Exploration Inductive Bias +2

Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

no code implementations5 Jun 2023 Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan

Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.

Graph Learning

Shrinking Embeddings for Hyper-Relational Knowledge Graphs

no code implementations3 Jun 2023 Bo Xiong, Mojtaba Nayyer, Shirui Pan, Steffen Staab

Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability.

Knowledge Graphs Link Prediction

A Survey on Fairness-aware Recommender Systems

no code implementations1 Jun 2023 Di Jin, Luzhi Wang, He Zhang, Yizhen Zheng, Weiping Ding, Feng Xia, Shirui Pan

As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.

Decision Making Fairness +1

Learning Strong Graph Neural Networks with Weak Information

1 code implementation29 May 2023 Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.

Graph Learning

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

no code implementations21 May 2023 Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan

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.

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?

no code implementations11 May 2023 Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan

In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.

Time Series Forecasting

Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

1 code implementation10 May 2023 Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan

We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.

Decision Making Session-Based Recommendations +1

Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain

no code implementations4 May 2023 Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Reza Haffari, Van Nguyen, Shuang Yu

In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i. e., in the form of probabilistic knowledge graphs).

Knowledge Graphs

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.

Hierarchical Multi-label Classification Knowledge Graph Completion +1

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

1 code implementation17 Apr 2023 Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan

In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).

Meta-Learning Metric Learning

Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

no code implementations3 Mar 2023 Junbin Mao, Jin Liu, Hanhe Lin, Hulin Kuang, Shirui Pan, Yi Pan

To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).

Disease Prediction Graph Embedding +1

Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs

no code implementations23 Feb 2023 Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan

Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.

Graph Learning Neural Architecture Search

On the Interaction between Node Fairness and Edge Privacy in Graph Neural Networks

no code implementations30 Jan 2023 He Zhang, Xingliang Yuan, Quoc Viet Hung Nguyen, Shirui Pan

Existing studies have respectively explored the fairness and privacy of GNNs and exhibited that both fairness and privacy are at the cost of GNN performance.


Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

1 code implementation25 Nov 2022 Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan

Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.

Graph Representation Learning

Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

1 code implementation15 Nov 2022 Linhao Luo, Reza Haffari, Shirui Pan

Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.

Graph Mining Link Prediction

A Comprehensive Survey on Distributed Training of Graph Neural Networks

no code implementations10 Nov 2022 Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Shirui Pan, WenGuang Chen, Yuan Xie

In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced.

Deep Learning for Time Series Anomaly Detection: A Survey

1 code implementation9 Nov 2022 Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.

Anomaly Detection Time Series Anomaly Detection

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

1 code implementation8 Nov 2022 Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.

Contrastive Learning Data Augmentation +2

How Far are We from Robust Long Abstractive Summarization?

1 code implementation30 Oct 2022 Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan

For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones.

Abstractive Text Summarization

Unifying Graph Contrastive Learning with Flexible Contextual Scopes

1 code implementation17 Oct 2022 Yizhen Zheng, Yu Zheng, Xiaofei Zhou, Chen Gong, Vincent CS Lee, Shirui Pan

To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).

Contrastive Learning Graph Representation Learning +1

Rethinking Efficiency and Redundancy in Training Large-scale Graphs

no code implementations2 Sep 2022 Xin Liu, Xunbin Xiong, Mingyu Yan, Runzhen Xue, Shirui Pan, Xiaochun Ye, Dongrui Fan

Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.

Fast Heterogeneous Federated Learning with Hybrid Client Selection

no code implementations10 Aug 2022 Guangyuan Shen, Dehong Gao, Duanxiao Song, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou

We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction.

Federated Learning

Simple and Efficient Heterogeneous Graph Neural Network

2 code implementations6 Jul 2022 Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.

An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics

1 code implementation3 Jul 2022 Huan Yee Koh, Jiaxin Ju, Ming Liu, Shirui Pan

The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics.

Document Summarization

Geometry Contrastive Learning on Heterogeneous Graphs

1 code implementation25 Jun 2022 Shichao Zhu, Chuan Zhou, Anfeng Cheng, Shirui Pan, Shuaiqiang Wang, Dawei Yin, Bin Wang

Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.

Contrastive Learning Node Classification +3

Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation

no code implementations CVPR 2022 Mingjie Li, Wenjia Cai, Karin Verspoor, Shirui Pan, Xiaodan Liang, Xiaojun Chang

To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure.

Clinical Knowledge Medical Report Generation

Ultrahyperbolic Knowledge Graph Embeddings

no code implementations1 Jun 2022 Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.

Knowledge Graph Embeddings

CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning

1 code implementation30 May 2022 Di Jin, Luzhi Wang, Yizhen Zheng, Xiang Li, Fei Jiang, Wei Lin, Shirui Pan

As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.

Collaborative Filtering Graph Classification +4

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

no code implementations16 May 2022 He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei

Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.

Drug Discovery Edge-computing +4

Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version

1 code implementation29 Mar 2022 Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan

Such spatio-temporal agnostic models employ a shared parameter space irrespective of the time series locations and the time periods and they assume that the temporal patterns are similar across locations and do not evolve across time, which may not always hold, thus leading to sub-optimal results.

Time Series Forecasting

Paraphrasing Techniques for Maritime QA system

no code implementations21 Mar 2022 Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Van Nguyen, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li

In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.

MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

1 code implementation25 Feb 2022 Linhao Luo, Yumeng Li, Buyu Gao, Shuai Tang, Sinan Wang, Jiancheng Li, Tanchao Zhu, Jiancai Liu, Zhao Li, Shirui Pan

We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.

Projective Ranking-based GNN Evasion Attacks

no code implementations25 Feb 2022 He Zhang, Xingliang Yuan, Chuan Zhou, Shirui Pan

By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes.

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

1 code implementation17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Traffic Prediction

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.

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach

no code implementations11 Feb 2022 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen

Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.

Anomaly Detection Contrastive Learning

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

no code implementations10 Feb 2022 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie

Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.

Multi-Graph Fusion Networks for Urban Region Embedding

1 code implementation24 Jan 2022 Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang

Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.

Crime Prediction

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

Towards Unsupervised Deep Graph Structure Learning

1 code implementation17 Jan 2022 Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.

Contrastive Learning Graph structure learning

Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

no code implementations15 Jan 2022 Guangyuan Shen, Dehong Gao, Libin Yang, Fang Zhou, Duanxiao Song, Wei Lou, Shirui Pan

However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL.

Federated Learning

Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels

no code implementations NeurIPS 2021 Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong

Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.

Graph Attention Node Classification +1

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.

BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule

no code implementations CVPR 2022 Miao Zhang, Jilin Hu, Steven Su, Shirui Pan, Xiaojun Chang, Bin Yang, Gholamreza Haffari

Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.

Neural Architecture Search Variational Inference

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

no code implementations20 Nov 2021 Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.

Contrastive Learning Graph Representation Learning +1

Recent Advances in Neural-symbolic Systems: A Survey

no code implementations10 Nov 2021 Dongran Yu, Bo Yang, Dayou Liu, Hui Wang, Shirui Pan

In recent years, neural systems have displayed highly effective learning ability and superior perception intelligence, but have been found to lack cognitive ability with effective reasoning.

Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications

1 code implementation17 Oct 2021 Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan

We present and implement two types of attacks, i. e., training-based attacks and threshold-based attacks from different adversarial capabilities.

Graph Classification Inference Attack +1

Leveraging Information Bottleneck for Scientific Document Summarization

no code implementations Findings (EMNLP) 2021 Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan

This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.

Document Summarization Language Modelling +2

Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs

no code implementations29 Sep 2021 Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan

Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.

Graph structure learning Representation Learning +1

ConTIG: Continuous Representation Learning on Temporal Interaction Graphs

no code implementations27 Sep 2021 Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang

Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.

Link Prediction Node Classification +1

Robust Physical-World Attacks on Face Recognition

no code implementations20 Sep 2021 Xin Zheng, Yanbo Fan, Baoyuan Wu, Yong Zhang, Jue Wang, Shirui Pan

Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.

Adversarial Attack Adversarial Robustness +1

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

1 code implementation25 Aug 2021 Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis

This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.

iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients

1 code implementation21 Jun 2021 Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad, Reza Haffari

A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation.

Neural Architecture Search

Anomaly Detection in Dynamic Graphs via Transformer

1 code implementation18 Jun 2021 Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent CS Lee

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.

Anomaly Detection

Pseudo-Riemannian Graph Convolutional Networks

1 code implementation6 Jun 2021 Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab

Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.

Graph Reconstruction Inductive Bias +2

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1 code implementation12 May 2021 Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.

Contrastive Learning Graph Representation Learning

Graph Learning: A Survey

no code implementations3 May 2021 Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan Liu

In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.

BIG-bench Machine Learning Combinatorial Optimization +3

Learning Graph Neural Networks with Positive and Unlabeled Nodes

no code implementations8 Mar 2021 Man Wu, Shirui Pan, Lan Du, Xingquan Zhu

By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs.

Node Classification Transductive Learning

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

1 code implementation27 Feb 2021 Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis

Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.

Anomaly Detection Contrastive Learning +1

Task-adaptive Neural Process for User Cold-Start Recommendation

1 code implementation26 Feb 2021 Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang

In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).

Meta-Learning Recommendation Systems

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

no code implementations25 Feb 2021 Shaoxiong Ji, Teemu Saravirta, Shirui Pan, Guodong Long, Anwar Walid

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.

Federated Learning Meta-Learning +3

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

Graph Stochastic Neural Networks for Semi-supervised Learning

1 code implementation NeurIPS 2020 Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.

Classification General Classification +2

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

1 code implementation NeurIPS 2020 Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, ZongYuan Ge, Steven Su

A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.

Bilevel Optimization Neural Architecture Search

Cyclic Label Propagation for Graph Semi-supervised Learning

no code implementations24 Nov 2020 Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu

To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.

Node Classification

Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization

1 code implementation24 Oct 2020 Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client.

Anomaly Detection Model extraction

Graph Geometry Interaction Learning

1 code implementation NeurIPS 2020 Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang

To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.

Link Prediction Node Classification

SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline

no code implementations19 Oct 2020 Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan

The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.

Graph Clustering graph construction +4

Medical Code Assignment with Gated Convolution and Note-Code Interaction

no code implementations Findings (ACL) 2021 Shaoxiong Ji, Shirui Pan, Pekka Marttinen

However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.


Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification

no code implementations19 Sep 2020 Sheng Wan, Chen Gong, Shirui Pan, Jie Yang, Jian Yang

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification.

General Classification graph construction +2

Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning

no code implementations15 Sep 2020 Sheng Wan, Shirui Pan, Jian Yang, Chen Gong

Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph.

Multivariate Relations Aggregation Learning in Social Networks

no code implementations9 Aug 2020 Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia

Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.

Graph Learning Node Classification

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

1 code implementation1 Jul 2020 Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts.

Joint Entity and Relation Extraction

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

2 code implementations24 May 2020 Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.

Graph Learning Multivariate Time Series Forecasting +1

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

Learning Multi-level Weight-centric Features for Few-shot Learning

no code implementations28 Nov 2019 Mingjiang Liang, Shaoli Huang, Shirui Pan, Mingming Gong, Wei Liu

Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning.

Few-Shot Learning

Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

no code implementations26 Sep 2019 Sheng Wan, Chen Gong, Ping Zhong, Shirui Pan, Guangyu Li, Jian Yang

In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.

Classification General Classification +1

Efficient Novelty-Driven Neural Architecture Search

no code implementations22 Jul 2019 Miao Zhang, Huiqi Li, Shirui Pan, Taoping Liu, Steven Su

The best architecture obtained by our algorithm with the same search space achieves the state-of-the-art test error rate of 2. 51\% on CIFAR-10 with only 7. 5 hours search time in a single GPU, and a validation perplexity of 60. 02 and a test perplexity of 57. 36 on PTB.

Neural Architecture Search

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

7 code implementations31 May 2019 Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.

Temporal Sequences Traffic Prediction

DAGCN: Dual Attention Graph Convolutional Networks

1 code implementation4 Apr 2019 Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long

In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems.

General Classification Graph Classification +1

Learning Graph Embedding with Adversarial Training Methods

no code implementations4 Jan 2019 Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang

Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.

Graph Clustering Graph Embedding +2

A Comprehensive Survey on Graph Neural Networks

5 code implementations3 Jan 2019 Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.

BIG-bench Machine Learning Image Classification +2

Learning Private Neural Language Modeling with Attentive Aggregation

4 code implementations17 Dec 2018 Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang

Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.

Federated Learning Language Modelling

Binarized Attributed Network Embedding

2 code implementations ICDM 2018 Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang

To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.

Graph Embedding Link Prediction +2

Label Embedding with Partial Heterogeneous Contexts

no code implementations3 May 2018 Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan

In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.

Descriptive Image Classification

Adversarially Regularized Graph Autoencoder for Graph Embedding

4 code implementations13 Feb 2018 Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.

Graph Clustering Graph Embedding +1

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

1 code implementation14 Sep 2017 Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.

Natural Language Inference Sentence Embedding +1

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering

no code implementations19 Aug 2016 Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.

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