Search Results for author: Chuan Shi

Found 65 papers, 35 papers with code

Learning Social Graph for Inactive User Recommendation

1 code implementation8 May 2024 Nian Liu, Shen Fan, Ting Bai, Peng Wang, Mingwei Sun, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Chuan Shi

In this paper, we propose a novel social recommendation method called LSIR (\textbf{L}earning \textbf{S}ocial Graph for \textbf{I}nactive User \textbf{R}ecommendation) that learns an optimal social graph structure for social recommendation, especially for inactive users.

Graph structure learning Recommendation Systems

Less is More: on the Over-Globalizing Problem in Graph Transformers

1 code implementation2 May 2024 Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, Chuan Shi

Then we propose a novel Bi-Level Global Graph Transformer with Collaborative Training (CoBFormer), including the inter-cluster and intra-cluster Transformers, to prevent the over-globalizing problem while keeping the ability to extract valuable information from distant nodes.

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

no code implementations19 Mar 2024 Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi

The F3 are expected to statistically neutralize the sensitive bias in node representations and provide additional nonsensitive information.


BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences

1 code implementation14 Mar 2024 Sun Ao, Weilin Zhao, Xu Han, Cheng Yang, Zhiyuan Liu, Chuan Shi, Maosong Sun

Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences.

Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

1 code implementation NeurIPS 2023 Donglin Xia, Xiao Wang, Nian Liu, Chuan Shi

To address this challenge, we propose the Cluster Information Transfer (CIT) mechanism (Code available at https://github. com/BUPT-GAMMA/CITGNN), which can learn invariant representations for GNNs, thereby improving their generalization ability to various and unknown test graphs with structure shift.

Minimum Topology Attacks for Graph Neural Networks

no code implementations5 Mar 2024 Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du

To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node.

Endowing Pre-trained Graph Models with Provable Fairness

1 code implementation19 Feb 2024 Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi

Furthermore, with GraphPAR, we quantify whether the fairness of each node is provable, i. e., predictions are always fair within a certain range of sensitive attribute semantics.

Attribute Fairness +1

GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks

1 code implementation11 Feb 2024 Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, Chuan Shi

Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks.

Graph Question Answering Instruction Following +4

Graph Fairness Learning under Distribution Shifts

no code implementations30 Jan 2024 Yibo Li, Xiao Wang, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, Chuan Shi

Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.


Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization

1 code implementation18 Dec 2023 Tianrui Jia, Haoyang Li, Cheng Yang, Tao Tao, Chuan Shi

In this paper, we propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy, which is capable of jointly generating mixed multiple environments and capturing invariant patterns from the mixed graph data.

Graph Representation Learning Out-of-Distribution Generalization

A Generalized Neural Diffusion Framework on Graphs

no code implementations14 Dec 2023 Yibo Li, Xiao Wang, Hongrui Liu, Chuan Shi

In this paper, we propose a general diffusion equation framework with the fidelity term, which formally establishes the relationship between the diffusion process with more GNNs.

Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework

no code implementations23 Nov 2023 Chunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi

In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.

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

Graph Distillation with Eigenbasis Matching

1 code implementation13 Oct 2023 Yang Liu, Deyu Bo, Chuan Shi

The emerging graph distillation (GD) tackles this challenge by distilling a small synthetic graph to replace the real large graph, ensuring GNNs trained on real and synthetic graphs exhibit comparable performance.

Data-centric Graph Learning: A Survey

no code implementations8 Oct 2023 Yuxin Guo, Deyu Bo, Cheng Yang, Zhiyuan Lu, Zhongjian Zhang, Jixi Liu, Yufei Peng, Chuan Shi

Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models.

Graph Learning

Provable Training for Graph Contrastive Learning

1 code implementation NeurIPS 2023 Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi

To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better.

Contrastive Learning

Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

no code implementations24 Apr 2023 Nian Liu, Xiao Wang, Hui Han, Chuan Shi

Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously.

Contrastive Learning

Graph Mining for Cybersecurity: A Survey

no code implementations2 Apr 2023 Bo Yan, Cheng Yang, Chuan Shi, Yong Fang, Qi Li, Yanfang Ye, Junping Du

In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.

Graph Mining

Abnormal Event Detection via Hypergraph Contrastive Learning

no code implementations2 Apr 2023 Bo Yan, Cheng Yang, Chuan Shi, Jiawei Liu, Xiaochen Wang

AEHCL designs the intra-event and inter-event contrastive modules to exploit self-supervised AHIN information.

Contrastive Learning Event Detection +1

Specformer: Spectral Graph Neural Networks Meet Transformers

1 code implementation2 Mar 2023 Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao

To tackle these issues, we introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter.


A Survey on Spectral Graph Neural Networks

no code implementations11 Feb 2023 Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi

Graph neural networks (GNNs) have attracted considerable attention from the research community.

Graph Representation Learning

MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning

1 code implementation14 Dec 2022 Xumeng Gong, Cheng Yang, Chuan Shi

We argue that typical data augmentation techniques (e. g., edge dropping) in GCL cannot generate diverse enough contrastive views to filter out noises.

Contrastive Learning Data Augmentation +2

Directed Acyclic Graph Structure Learning from Dynamic Graphs

1 code implementation30 Nov 2022 Shaohua Fan, Shuyang Zhang, Xiao Wang, Chuan Shi

In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features.

Graph structure learning

xTrimoABFold: De novo Antibody Structure Prediction without MSA

no code implementations30 Nov 2022 Yining Wang, Xumeng Gong, Shaochuan Li, Bing Yang, YiWu Sun, Chuan Shi, Yangang Wang, Cheng Yang, Hui Li, Le Song

Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.

Computational Efficiency Protein Language Model

Uncovering the Structural Fairness in Graph Contrastive Learning

1 code implementation6 Oct 2022 Ruijia Wang, Xiao Wang, Chuan Shi, Le Song

Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world.

Contrastive Learning Fairness

Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum

1 code implementation5 Oct 2022 Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei

Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works.

Contrastive Learning

Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

1 code implementation28 Sep 2022 Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang

However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists.

counterfactual Graph Classification

KGNN: Distributed Framework for Graph Neural Knowledge Representation

no code implementations17 May 2022 Binbin Hu, Zhiyang Hu, Zhiqiang Zhang, Jun Zhou, Chuan Shi

Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services.

Attribute Decoder +2

Data-Free Adversarial Knowledge Distillation for Graph Neural Networks

no code implementations8 May 2022 Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, Lichao Sun

Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications.

Generative Adversarial Network Graph Classification +3

An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022

1 code implementation1 Mar 2022 Qian Zhao, Shuo Yang, Binbin Hu, Zhiqiang Zhang, Yakun Wang, Yusong Chen, Jun Zhou, Chuan Shi

Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.

Attribute Graph Learning +1

Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network

1 code implementation18 Feb 2022 Tianyu Zhao, Cheng Yang, Yibo Li, Quan Gan, Zhenyi Wang, Fengqi Liang, Huan Zhao, Yingxia Shao, Xiao Wang, Chuan Shi

Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied scenarios.

Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift

1 code implementation27 Jan 2022 Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, Jun Zhou

To this end, in this paper, we propose a novel Distribution Recovered Graph Self-Training framework (DR-GST), which could recover the distribution of the original labeled dataset.

Variational Inference

Debiased Graph Neural Networks with Agnostic Label Selection Bias

no code implementations19 Jan 2022 Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang

Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.

Selection bias

Compact Graph Structure Learning via Mutual Information Compression

2 code implementations14 Jan 2022 Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, Chuan Shi

Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.

Graph structure learning

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs

1 code implementation20 Nov 2021 Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang

Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.

Causal Inference

Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration

2 code implementations NeurIPS 2021 Xiao Wang, Hongrui Liu, Chuan Shi, Cheng Yang

Specifically, we first verify that the confidence distribution in a graph has homophily property, and this finding inspires us to design a calibration GNN model (CaGCN) to learn the calibration function.

Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

3 code implementations19 May 2021 Xiao Wang, Nian Liu, Hui Han, Chuan Shi

Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views.

Contrastive Learning

Lorentzian Graph Convolutional Networks

no code implementations15 Apr 2021 Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, Guojie Song

We also find that the performance of some hyperbolic GCNs can be improved by simply replacing the graph operations with those we defined in this paper.

Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

1 code implementation4 Mar 2021 Cheng Yang, Jiawei Liu, Chuan Shi

Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model.

Knowledge Distillation Node Classification

Interpreting and Unifying Graph Neural Networks with An Optimization Framework

1 code implementation28 Jan 2021 Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui

Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks.

Beyond Low-frequency Information in Graph Convolutional Networks

1 code implementation4 Jan 2021 Deyu Bo, Xiao Wang, Chuan Shi, HuaWei Shen

For a deeper understanding, we theoretically analyze the roles of low-frequency signals and high-frequency signals on learning node representations, which further explains why FAGCN can perform well on different types of networks.

Node Classification on Non-Homophilic (Heterophilic) Graphs

Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks

no code implementations1 Jan 2021 Xiaojun Ma, Ziyao Li, Lingjun Xu, Guojie Song, Yi Li, Chuan Shi

To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs).

Learning Node Representations from Noisy Graph Structures

no code implementations4 Dec 2020 Junshan Wang, Ziyao Li, Qingqing Long, Weiyu Zhang, Guojie Song, Chuan Shi

Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.

Graph Reconstruction Node Classification

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.

Clustering Graph Classification +5

AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

no code implementations5 Jul 2020 Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei

We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).

General Classification

Graph Neural News Recommendation with Unsupervised Preference Disentanglement

1 code implementation ACL 2020 Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.

Disentanglement News Recommendation

Decorrelated Clustering with Data Selection Bias

1 code implementation29 Jun 2020 Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang

Most of existing clustering algorithms are proposed without considering the selection bias in data.

Clustering Selection bias

Structural Deep Clustering Network

2 code implementations5 Feb 2020 Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui

The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.

Clustering Deep Clustering +1

Hyperbolic Graph Attention Network

1 code implementation6 Dec 2019 Yiding Zhang, Xiao Wang, Xunqiang Jiang, Chuan Shi, Yanfang Ye

Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently.

Anatomy Graph Attention

Multi-Component Graph Convolutional Collaborative Filtering

1 code implementation25 Nov 2019 Xiao Wang, Ruijia Wang, Chuan Shi, Guojie Song, Qingyong Li

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.

Collaborative Filtering Recommendation Systems

GraLSP: Graph Neural Networks with Local Structural Patterns

no code implementations18 Nov 2019 Yilun Jin, Guojie Song, Chuan Shi

Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns.

Graph Representation Learning

Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification

no code implementations IJCNLP 2019 Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, Xiao-Li Li

Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions.

General Classification Graph Attention +3

Graph Neural News Recommendation with Long-term and Short-term Interest Modeling

no code implementations30 Oct 2019 Linmei Hu, Chen Li, Chuan Shi, Cheng Yang, Chao Shao

Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history.

Collaborative Filtering News Recommendation +1

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

no code implementations14 Sep 2019 Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu

However, the characteristics of users and the properties of items may stem from different aspects, e. g., the brand-aspect and category-aspect of items.

Collaborative Filtering Recommendation Systems

Temporal Network Embedding with Micro- and Macro-dynamics

1 code implementation10 Sep 2019 Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye

The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale.

Network Embedding

Relation Structure-Aware Heterogeneous Information Network Embedding

no code implementations15 May 2019 Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu

In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE).

Clustering Link Prediction +4

Heterogeneous Graph Attention Network

3 code implementations WWW 2019 2019 Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye

With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.

Social and Information Networks

Heterogeneous Information Network Embedding for Recommendation

1 code implementation29 Nov 2017 Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu

In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.

Social and Information Networks

HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

no code implementations28 Sep 2013 Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu

Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type.

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