Search Results for author: Jundong Li

Found 116 papers, 61 papers with code

A Survey of Scaling in Large Language Model Reasoning

no code implementations2 Apr 2025 Zihan Chen, Song Wang, Zhen Tan, Xingbo Fu, Zhenyu Lei, Peng Wang, Huan Liu, Cong Shen, Jundong Li

The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration.

Language Modeling Language Modelling +2

Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models

1 code implementation30 Mar 2025 Haochen Liu, Song Wang, Chen Chen, Jundong Li

Recent approaches leverage Graph Neural Networks (GNNs) to generate KG-based input embedding prefixes as soft prompts for LLMs but fail to account for question relevance, resulting in noisy prompts.

Knowledge Graphs Multiple-choice +1

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

no code implementations28 Mar 2025 Song Wang, Junhong Lin, Xiaojie Guo, Julian Shun, Jundong Li, Yada Zhu

In this paper, we propose the ReKnoS framework, which aims to Reason over Knowledge Graphs with Super-Relations.

Knowledge Graphs Retrieval

Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion

1 code implementation1 Feb 2025 Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen, Jundong Li

In this paper, we introduce rotation symmetry, a novel form of parameter space symmetry for transformers that generalizes permutation symmetry by rotating parameter matrices in self-attention layers.

Collaborative Diffusion Model for Recommender System

no code implementations31 Jan 2025 Gyuseok Lee, Yaochen Zhu, Hwanjo Yu, Yao Zhou, Jundong Li

Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities.

Denoising model +1

Harnessing Large Language Models for Disaster Management: A Survey

no code implementations12 Jan 2025 Zhenyu Lei, Yushun Dong, Weiyu Li, Rong Ding, Qi Wang, Jundong Li

Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields.

Management Survey

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization

no code implementations7 Jan 2025 Song Wang, Xiaodong Yang, Rashidul Islam, Huiyuan Chen, Minghua Xu, Jundong Li, Yiwei Cai

These methods often employ a two-step strategy that first creates augmented environments and subsequently identifies invariant subgraphs to improve generalizability.

Out-of-Distribution Generalization

KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models

no code implementations6 Jan 2025 Zaiyi Zheng, Yushun Dong, Song Wang, Haochen Liu, Qi Wang, Jundong Li

Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC).

Knowledge Graph Completion

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning

1 code implementation26 Dec 2024 Xingbo Fu, Zihan Chen, Yinhan He, Song Wang, Binchi Zhang, Chen Chen, Jundong Li

In the real world, however, the graph data can suffer from significant distribution shifts across clients as the clients may collect their graph data for different purposes.

Graph Learning Graph Property Prediction +1

BrainMAP: Learning Multiple Activation Pathways in Brain Networks

2 code implementations23 Dec 2024 Song Wang, Zhenyu Lei, Zhen Tan, Jiaqi Ding, Xinyu Zhao, Yushun Dong, Guorong Wu, Tianlong Chen, Chen Chen, Aiying Zhang, Jundong Li

As such, conventional GNNs struggle to learn from these pathways due to the long-range dependencies of multiple pathways.

Mamba

ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data

1 code implementation14 Dec 2024 Zhenyu Lei, Yushun Dong, Jundong Li, Chen Chen

However, in real-world applications, most nodes may not possess any available temporal data during training.

Data Augmentation

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective

1 code implementation10 Dec 2024 Yushun Dong, Patrick Soga, Yinhan He, Song Wang, Jundong Li

To demystify such a conflict, this paper introduces a comprehensive benchmark to measure and evaluate GNNs' capability in capturing and leveraging the information encoded in different frequency components of the input graph data.

Benchmarking

Instance-Aware Graph Prompt Learning

no code implementations26 Nov 2024 Jiazheng Li, Jundong Li, Chuxu Zhang

Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availability of high-quality labels in an end-to-end manner.

Graph Representation Learning

Invariant Shape Representation Learning For Image Classification

1 code implementation19 Nov 2024 Tonmoy Hossain, Jing Ma, Jundong Li, Miaomiao Zhang

In this paper, we introduce a novel framework that for the first time develops invariant shape representation learning (ISRL) to further strengthen the robustness of image classifiers.

Classification Image Classification +1

Federated Graph Learning with Graphless Clients

no code implementations13 Nov 2024 Xingbo Fu, Song Wang, Yushun Dong, Binchi Zhang, Chen Chen, Jundong Li

To enable structure knowledge transfer, we design a GNN model and a feature encoder on each client.

Graph Learning Knowledge Distillation +1

A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation

2 code implementations25 Oct 2024 Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding

Consequently, there has been a surge in research on graph machine learning under distribution shifts, aiming to train models to achieve satisfactory performance on out-of-distribution (OOD) test data.

Graph Learning Out-of-Distribution Generalization

Global Graph Counterfactual Explanation: A Subgraph Mapping Approach

no code implementations25 Oct 2024 Yinhan He, Wendy Zheng, Yaochen Zhu, Jing Ma, Saumitra Mishra, Natraj Raman, Ninghao Liu, Jundong Li

Methodologically, we design a significant subgraph generator and a counterfactual subgraph autoencoder in our GlobalGCE, where the subgraphs and the rules can be effectively generated.

counterfactual Counterfactual Explanation

Channel-Wise Mixed-Precision Quantization for Large Language Models

no code implementations16 Oct 2024 Zihan Chen, Bike Xie, Jundong Li, Cong Shen

Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes.

Quantization

Federated Graph Learning with Structure Proxy Alignment

1 code implementation18 Aug 2024 Xingbo Fu, Zihan Chen, Binchi Zhang, Chen Chen, Jundong Li

Moreover, FGL also encounters a unique challenge for the node classification task: the nodes from a minority class in a client are more likely to have biased neighboring information, which prevents FGL from learning expressive node embeddings with Graph Neural Networks (GNNs).

Federated Learning Fraud Detection +2

Understanding and Modeling Job Marketplace with Pretrained Language Models

no code implementations8 Aug 2024 Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li

Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs.

Graph Neural Network

Towards Certified Unlearning for Deep Neural Networks

1 code implementation1 Aug 2024 Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li

In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees.

Machine Unlearning

Verification of Machine Unlearning is Fragile

1 code implementation1 Aug 2024 Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li

These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model.

Machine Unlearning

IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks

no code implementations28 Jul 2024 Yushun Dong, Binchi Zhang, Zhenyu Lei, Na Zou, Jundong Li

Specifically, we first instantiate four types of unlearning requests on graphs, and then we propose an approximation approach to flexibly handle these unlearning requests over diverse GNNs.

Machine Unlearning

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models

no code implementations2 Jul 2024 Song Wang, Peng Wang, Tong Zhou, Yushun Dong, Zhen Tan, Jundong Li

To address these limitations, we collect a variety of datasets designed for the bias evaluation of LLMs, and further propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.

Fairness

"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models

no code implementations26 Jun 2024 Zhen Tan, Chengshuai Zhao, Raha Moraffah, YiFan Li, Song Wang, Jundong Li, Tianlong Chen, Huan Liu

Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases, improving their performance in applications like fact-checking and information searching.

Fact Checking RAG +1

Causal Inference with Latent Variables: Recent Advances and Future Prospectives

no code implementations20 Jun 2024 Yaochen Zhu, Yinhan He, Jing Ma, Mengxuan Hu, Sheng Li, Jundong Li

Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc.

Causal Discovery Causal Inference +2

Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation

1 code implementation19 Jun 2024 Haochen Liu, Song Wang, Chen Chen, Jundong Li

To overcome these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational Reasoning), a novel approach that effectively adapts the information in contextualized graphs to various subgraphs generated from support and query triplets to perform the prediction.

Knowledge Graphs Relational Reasoning

Knowledge Graph-Enhanced Large Language Models via Path Selection

1 code implementation19 Jun 2024 Haochen Liu, Song Wang, Yaochen Zhu, Yushun Dong, Jundong Li

In addition, LLMs tend to pick only knowledge with direct semantic relationship with the input text, while potentially useful knowledge with indirect semantics can be ignored.

Hallucination Knowledge Graphs

Cognitively Inspired Energy-Based World Models

no code implementations13 Jun 2024 Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Aman Chadha, Jundong Li, Tariq Iqbal

One of the predominant methods for training world models is autoregressive prediction in the output space of the next element of a sequence.

FastGAS: Fast Graph-based Annotation Selection for In-Context Learning

no code implementations6 Jun 2024 Zihan Chen, Song Wang, Cong Shen, Jundong Li

By aggregating nodes from diverse pieces and annotating the corresponding instances, we identify a set of diverse and representative instances for ICL.

graph partitioning In-Context Learning

Spectral Greedy Coresets for Graph Neural Networks

no code implementations27 May 2024 Mucong Ding, Yinhan He, Jundong Li, Furong Huang

However, owing to the interdependence of graph nodes, coreset selection, which selects subsets of the data examples, has not been successfully applied to speed up GNN training on large graphs, warranting special treatment.

Dataset Condensation Node Classification

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

Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era

1 code implementation13 Mar 2024 Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, Ninghao Liu

Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI.

GraphRCG: Self-Conditioned Graph Generation

no code implementations2 Mar 2024 Song Wang, Zhen Tan, Xinyu Zhao, Tianlong Chen, Huan Liu, Jundong Li

In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process.

Graph Generation

Large Language Models for Data Annotation and Synthesis: A Survey

1 code implementation21 Feb 2024 Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu

Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.

Survey

Efficient Prompt Optimization Through the Lens of Best Arm Identification

no code implementations15 Feb 2024 Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen

TRIPLE is built on a novel connection established between prompt optimization and fixed-budget best arm identification (BAI-FB) in multi-armed bandits (MAB); thus, it is capable of leveraging the rich toolbox from BAI-FB systematically and also incorporating unique characteristics of prompt optimization.

Instruction Following Multi-Armed Bandits

Personalized Federated Learning with Attention-based Client Selection

no code implementations23 Dec 2023 Zihan Chen, Jundong Li, Cong Shen

FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions and mitigate the data scarcity issue.

Personalized Federated Learning

Interpreting Pretrained Language Models via Concept Bottlenecks

1 code implementation8 Nov 2023 Zhen Tan, Lu Cheng, Song Wang, Yuan Bo, Jundong Li, Huan Liu

Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.

Certified Defense on the Fairness of Graph Neural Networks

1 code implementation5 Nov 2023 Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li

Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years.

Fairness Graph Learning

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

no code implementations2 Nov 2023 Song Wang, Zhen Tan, Ruocheng Guo, Jundong Li

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing.

Collaborative Large Language Model for Recommender Systems

1 code implementation2 Nov 2023 Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li

We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics.

Hallucination Language Modeling +4

Knowledge Editing for Large Language Models: A Survey

no code implementations24 Oct 2023 Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li

Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category.

knowledge editing Survey

Marginal Nodes Matter: Towards Structure Fairness in Graphs

no code implementations23 Oct 2023 Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou

Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.

Fairness

ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt

1 code implementation23 Oct 2023 Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, Jianling Sun

Based on this framework, we propose a prompt-based transferability test to find the most relevant pretext task in order to reduce the semantic gap.

Multi-Task Learning Position

Adversarial Attacks on Fairness of Graph Neural Networks

1 code implementation20 Oct 2023 Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e. g., female) in graph-based applications.

Fairness

Fair Few-shot Learning with Auxiliary Sets

no code implementations28 Aug 2023 Song Wang, Jing Ma, Lu Cheng, Jundong Li

These auxiliary sets contain several labeled training samples that can enhance the model performance regarding fairness in meta-test tasks, thereby allowing for the transfer of learned useful fairness-oriented knowledge to meta-test tasks.

Fairness Few-Shot Learning

GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction

1 code implementation18 Aug 2023 Yucheng Shi, Yushun Dong, Qiaoyu Tan, Jundong Li, Ninghao Liu

By considering embeddings encompassing graph topology and attribute information as reconstruction targets, our model could capture more generalized and comprehensive knowledge.

Attribute Self-Supervised Learning

Collaborative Graph Neural Networks for Attributed Network Embedding

1 code implementation22 Jul 2023 Qiaoyu Tan, Xin Zhang, Xiao Huang, Hao Chen, Jundong Li, Xia Hu

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.

Attribute Network Embedding

Learning for Counterfactual Fairness from Observational Data

no code implementations17 Jul 2023 Jing Ma, Ruocheng Guo, Aidong Zhang, Jundong Li

A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data.

Attribute Causal Discovery +4

Contrastive Meta-Learning for Few-shot Node Classification

1 code implementation27 Jun 2023 Song Wang, Zhen Tan, Huan Liu, Jundong Li

First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes.

Classification Graph Mining +2

Federated Few-shot Learning

1 code implementation17 Jun 2023 Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li

In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients.

Federated Learning Few-Shot Learning

Path-Specific Counterfactual Fairness for Recommender Systems

1 code implementation5 Jun 2023 Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li

But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.

Blocking counterfactual +4

Spatial-Temporal Networks for Antibiogram Pattern Prediction

no code implementations2 May 2023 Xingbo Fu, Chen Chen, Yushun Dong, Anil Vullikanti, Eili Klein, Gregory Madden, Jundong Li

In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.

Prediction

When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?

no code implementations2 May 2023 Yushun Dong, Jundong Li, Tobias Schnabel

In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation.

Memorization Recommendation Systems

Few-shot Node Classification with Extremely Weak Supervision

1 code implementation6 Jan 2023 Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li

Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i. e., meta-training classes) and then generalize to classes with limited labeled nodes (i. e., meta-test classes).

Classification Meta-Learning +1

Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization

1 code implementation3 Jan 2023 Yaochen Zhu, Jing Ma, Jundong Li

Traditional RSs estimate user interests and predict their future behaviors by utilizing correlations in the observational historical activities, their profiles, and the content of interacted items.

Causal Inference Recommendation Systems

RELIANT: Fair Knowledge Distillation for Graph Neural Networks

1 code implementation3 Jan 2023 Yushun Dong, Binchi Zhang, Yiling Yuan, Na Zou, Qi Wang, Jundong Li

Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i. e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i. e., the teacher GNN model).

Fairness Graph Learning +1

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

1 code implementation11 Dec 2022 Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.

Classification Contrastive Learning +4

Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

1 code implementation25 Nov 2022 Yushun Dong, Song Wang, Jing Ma, Ninghao Liu, Jundong Li

In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes.

Graph Few-shot Learning with Task-specific Structures

1 code implementation21 Oct 2022 Song Wang, Chen Chen, Jundong Li

Therefore, to adaptively learn node representations across meta-tasks, we propose a novel framework that learns a task-specific structure for each meta-task.

Classification Few-Shot Learning +2

CLEAR: Generative Counterfactual Explanations on Graphs

no code implementations16 Oct 2022 Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li

Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?".

counterfactual Counterfactual Explanation +1

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

BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency

1 code implementation17 Aug 2022 Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo

In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.

Misinformation Twitter Bot Detection

KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion

1 code implementation16 Aug 2022 Zhaoxuan Tan, Zilong Chen, Shangbin Feng, Qingyue Zhang, Qinghua Zheng, Jundong Li, Minnan Luo

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.

Contrastive Learning Knowledge Graph Embeddings

Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications

no code implementations24 Jul 2022 Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li

Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner.

BIG-bench Machine Learning

Learning Causal Effects on Hypergraphs

no code implementations7 Jul 2022 Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, Jaime Teevan

Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes.

On Structural Explanation of Bias in Graph Neural Networks

1 code implementation24 Jun 2022 Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li

The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.

Decision Making Fairness

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

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

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

1 code implementation7 Jun 2022 Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, Tyler Derr

Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.

Attribute Fairness +2

Towards Explanation for Unsupervised Graph-Level Representation Learning

1 code implementation20 May 2022 Qinghua Zheng, Jihong Wang, Minnan Luo, YaoLiang Yu, Jundong Li, Lina Yao, Xiaojun Chang

Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"

Decision Making Graph Classification +2

FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

1 code implementation5 May 2022 Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li

Specifically, these works propose to accumulate meta-knowledge across diverse meta-training tasks, and then generalize such meta-knowledge to the target task with a disjoint label set.

Few-Shot Learning Graph Classification

Empowering Next POI Recommendation with Multi-Relational Modeling

no code implementations24 Apr 2022 Zheng Huang, Jing Ma, Yushun Dong, Natasha Zhang Foutz, Jundong Li

Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations).

Representation Learning

Fairness in Graph Mining: A Survey

2 code implementations21 Apr 2022 Yushun Dong, Jing Ma, Song Wang, Chen Chen, Jundong Li

Recently, algorithmic fairness has been extensively studied in graph-based applications.

Fairness Graph Mining +1

KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media

1 code implementation NAACL 2022 Wenqian Zhang, Shangbin Feng, Zilong Chen, Zhenyu Lei, Jundong Li, Minnan Luo

Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles.

Knowledge Graphs Representation Learning

Self-Supervised Learning for Recommender Systems: A Survey

1 code implementation29 Mar 2022 Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, Zi Huang

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data.

Recommendation Systems Self-Supervised Learning +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 +2

Geometric Graph Representation Learning via Maximizing Rate Reduction

no code implementations13 Feb 2022 Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu

Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.

Community Detection Contrastive Learning +3

Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

no code implementations21 Jan 2022 Jihong Wang, Minnan Luo, Jundong Li, Ziqi Liu, Jun Zhou, Qinghua Zheng

Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph.

Adversarial Attack Graph Learning +2

Learning Fair Node Representations with Graph Counterfactual Fairness

1 code implementation10 Jan 2022 Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang, Aidong Zhang, Jundong Li

In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes.

Attribute counterfactual +2

Unbiased Graph Embedding with Biased Graph Observations

no code implementations26 Oct 2021 Nan Wang, Lu Lin, Jundong Li, Hongning Wang

In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes.

Fairness Graph Embedding

Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation

no code implementations29 Sep 2021 Han Yue, Jundong Li, Hongfu Liu

Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information.

feature selection

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

1 code implementation9 Sep 2021 Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, Hongzhi Yin

Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.

EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

1 code implementation11 Aug 2021 Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li

We then develop a framework EDITS to mitigate the bias in attributed networks while maintaining the performance of GNNs in downstream tasks.

Decision Making Fraud Detection

Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification

no code implementations12 Jun 2021 Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu

Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.

Classification Drug Design +5

Fairness-Aware Unsupervised Feature Selection

no code implementations4 Jun 2021 Xiaoying Xing, Hongfu Liu, Chen Chen, Jundong Li

Feature selection is a prevalent data preprocessing paradigm for various learning tasks.

Fairness feature selection

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US

1 code implementation29 May 2021 Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, Jundong Li

Besides, as the confounders may be time-varying during COVID-19 (e. g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them.

Automated Generation of Interorganizational Disaster Response Networks through Information Extraction

no code implementations27 Feb 2021 Yitong Li, Duoduo Liao, Jundong Li, Wenying Ji

When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders.

Disaster Response named-entity-recognition +2

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

4 code implementations16 Jan 2021 Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang

In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.

Recommendation Systems Self-Supervised Learning

Line Graph Neural Networks for Link Prediction

2 code implementations20 Oct 2020 Lei Cai, Jundong Li, Jie Wang, Shuiwang Ji

In this formalism, a link prediction problem is converted to a graph classification task.

General Classification Graph Classification +3

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

2 code implementations23 Jun 2020 Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu

By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.

Classification Drug Discovery +5

Scalable Attack on Graph Data by Injecting Vicious Nodes

1 code implementation22 Apr 2020 Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, Qinghua Zheng

Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks

no code implementations5 Apr 2020 Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data.

Recommendation Systems

Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

no code implementations5 Mar 2020 Min Gao, Junwei Zhang, Junliang Yu, Jundong Li, Junhao Wen, Qingyu Xiong

In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy.

Data Augmentation Recommendation Systems

Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

no code implementations22 Dec 2019 Ruocheng Guo, Jundong Li, Huan Liu

When such data comes with network information, the later can be potentially useful to correct hidden confounding bias.

Causal Inference counterfactual +1

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

no code implementations8 Sep 2019 Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.

Recommendation Systems

Feature Interaction-aware Graph Neural Networks

no code implementations19 Aug 2019 Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu, Huan Liu

Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.

Graph Learning Representation Learning

Deep Structured Cross-Modal Anomaly Detection

no code implementations11 Aug 2019 Yuening Li, Ninghao Liu, Jundong Li, Mengnan Du, Xia Hu

To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.

Anomaly Detection

SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

no code implementations11 Aug 2019 Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou

SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.

Anomaly Detection Density Estimation

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

1 code implementation17 Jul 2019 Yuxin Ma, Tiankai Xie, Jundong Li, Ross Maciejewski

Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and numerous other critical tasks.

BIG-bench Machine Learning Data Poisoning

Learning Individual Causal Effects from Networked Observational Data

1 code implementation8 Jun 2019 Ruocheng Guo, Jundong Li, Huan Liu

In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders.

Causal Inference

Deep Anomaly Detection on Attributed Networks

2 code implementations 2019 SIAM International Conference on Data Mining (SDM) 2019 Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu

In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.

Anomaly Detection Attribute

Online Newton Step Algorithm with Estimated Gradient

no code implementations25 Nov 2018 Binbin Liu, Jundong Li, Yunquan Song, Xijun Liang, Ling Jian, Huan Liu

In particular, we extend the ONS algorithm with the trick of expected gradient and develop a novel second-order online learning algorithm, i. e., Online Newton Step with Expected Gradient (ONSEG).

A Survey of Learning Causality with Data: Problems and Methods

3 code implementations25 Sep 2018 Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.

BIG-bench Machine Learning Survey

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

2 code implementations ASONAM 2019 2019 Jundong Li, Liang Wu, Huan Liu

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

Ensemble Learning Feature Engineering +1

Attributed Network Embedding for Learning in a Dynamic Environment

no code implementations6 Jun 2017 Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu

To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.

Attribute Clustering +3

Challenges of Feature Selection for Big Data Analytics

no code implementations7 Nov 2016 Jundong Li, Huan Liu

We are surrounded by huge amounts of large-scale high dimensional data.

feature selection

Feature Selection: A Data Perspective

2 code implementations29 Jan 2016 Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, Huan Liu

To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).

feature selection Sparse Learning +1

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