Search Results for author: Chuxu Zhang

Found 37 papers, 22 papers with code

Safety in Graph Machine Learning: Threats and Safeguards

no code implementations17 May 2024 Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen, Xingbo Fu, Yinhan He, Cong Shen, Chuxu Zhang, Nitesh V. Chawla, Jundong Li

In this survey paper, we explore three critical aspects vital for enhancing safety in Graph ML: reliability, generalizability, and confidentiality.

Fraud Detection

Dual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement

1 code implementation International World Wide Web Conference 2024 Yiyue Qian, Tianyi Ma, Chuxu Zhang, Yanfang Ye

However, these works have the following limitations in modeling the high-order relationships over unlabeled data: (i) They primarily focus on maximizing the agreements among individual node embeddings while neglecting the capture of group-wise collective behaviors within hypergraphs; (ii) Most of them disregard the importance of the temperature index in discriminating contrastive pairs during contrast optimization.

Contrastive Learning Hypergraph Contrastive Learning +1

Subgraph Pooling: Tackling Negative Transfer on Graphs

1 code implementation14 Feb 2024 Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Unlike in image or text data, we find that negative transfer could commonly occur in graph-structured data, even when source and target graphs have semantic similarities.

Transfer Learning

Graph Inference Acceleration by Learning MLPs on Graphs without Supervision

1 code implementation14 Feb 2024 Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye

Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph learning tasks, yet their reliance on message-passing constraints their deployment in latency-sensitive applications such as financial fraud detection.

Fraud Detection Graph Learning

UGMAE: A Unified Framework for Graph Masked Autoencoders

no code implementations12 Feb 2024 Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, Nitesh V. Chawla

In light of this, we propose UGMAE, a unified framework for graph masked autoencoders to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency.

Self-Supervised Learning

Hypergraph Contrastive Learning for Drug Trafficking Community Detection

1 code implementation 2023 IEEE International Conference on Data Mining (ICDM) 2023 Tianyi Ma, Yiyue Qian, Chuxu Zhang, Yanfang Ye

To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities.

Attribute Community Detection +3

Multi-Modal Self-Supervised Learning for Recommendation

2 code implementations21 Feb 2023 Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations.

Contrastive Learning Data Augmentation +2

Knowledge Distillation on Graphs: A Survey

no code implementations1 Feb 2023 Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla

Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.

Knowledge Distillation Model Compression

Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

1 code implementation19 Nov 2022 Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye

Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model.

Product Recommendation

Self-Supervised Graph Structure Refinement for Graph Neural Networks

1 code implementation12 Nov 2022 Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye

Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks.

Contrastive Learning Graph structure learning +1

Boosting Graph Neural Networks via Adaptive Knowledge Distillation

no code implementations12 Oct 2022 Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh Chawla

In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.

Graph Classification Graph Mining +3

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

1 code implementation6 Oct 2022 Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, Yanfang Ye

In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA.

Entity Embeddings Graph Neural Network +1

Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization

1 code implementation5 Oct 2022 Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, Chuxu Zhang

Besides, we observe that learning from multiple philosophies enhances not only the task generalization but also the single task performances, demonstrating that PARETOGNN achieves better task generalization via the disjoint yet complementary knowledge learned from different philosophies.

Link Prediction Node Classification +4

Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning

no code implementations1 Oct 2022 Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang

The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.

Contrastive Learning Graph Representation Learning

Contrastive Graph Few-Shot Learning

no code implementations30 Sep 2022 Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye, Chuxu Zhang

Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss.

Contrastive Learning Few-Shot Learning +2

Graph Contrastive Learning with Cross-view Reconstruction

no code implementations16 Sep 2022 Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Yanfang Ye, Chuxu Zhang

In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GraphCV), which follows the information bottleneck principle to learn minimal yet sufficient representation from graph data.

Contrastive Learning Disentanglement +3

NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs

1 code implementation22 Aug 2022 Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla

Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs.

Heterogeneous Graph Masked Autoencoders

1 code implementation21 Aug 2022 Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla

In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges.

Attribute Self-Supervised Learning

Self-Supervised Hypergraph Transformer for Recommender Systems

1 code implementation28 Jul 2022 Lianghao Xia, Chao Huang, Chuxu Zhang

With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems.

Collaborative Filtering Data Augmentation +2

Task-Adaptive Few-shot Node Classification

1 code implementation23 Jun 2022 Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, Jundong Li

Then we transfer such knowledge to the classes with limited labeled nodes via our proposed task-adaptive modules.

Classification Few-Shot Learning +2

Label-invariant Augmentation for Semi-Supervised Graph Classification

no code implementations19 May 2022 Han Yue, Chunhui Zhang, Chuxu Zhang, Hongfu Liu

Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generalization and robustness.

Contrastive Learning Graph Classification +1

Few-Shot Learning on Graphs

no code implementations17 Mar 2022 Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.

Few-Shot Learning Graph Mining +1

Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media

1 code implementation NeurIPS 2021 Yiyue Qian, Yiming Zhang, Yanfang Ye, Chuxu Zhang

In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers on social media (i. e., Instagram), by tackling the following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG is capable of jointly modeling multi-modal content and relational structured information on social media for illicit drug trafficker detection; (2) in addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training.

Knowledge Distillation Marketing +3

Heterogeneous Temporal Graph Neural Network

1 code implementation26 Oct 2021 Yujie Fan, Mingxuan Ju, Chuxu Zhang, Liang Zhao, Yanfang Ye

To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG.

Graph Neural Network Relation +1

A Simple and Debiased Sampling Method for Personalized Ranking

no code implementations29 Sep 2021 Lu Yu, Shichao Pei, Chuxu Zhang, Xiangliang Zhang

Pairwise ranking models have been widely used to address various problems, such as recommendation.

Few-Shot Graph Learning for Molecular Property Prediction

1 code implementation16 Feb 2021 Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.

Attribute Drug Discovery +8

SAIL: Self-Augmented Graph Contrastive Learning

no code implementations2 Sep 2020 Lu Yu, Shichao Pei, Lizhong Ding, Jun Zhou, Longfei Li, Chuxu Zhang, Xiangliang Zhang

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.

Contrastive Learning Knowledge Distillation +1

Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning

no code implementations12 Mar 2020 Mandana Saebi, Steven Krieg, Chuxu Zhang, Meng Jiang, Nitesh Chawla

Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems.

Graph Neural Network Knowledge Graphs +5

Few-Shot Knowledge Graph Completion

1 code implementation26 Nov 2019 Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.

One-Shot Learning Relation

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

5 code implementations20 Nov 2018 Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

Decoder Time Series +2

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