Search Results for author: Jundong Li

Found 67 papers, 39 papers with code

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.

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.

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.

Causal Discovery Data Augmentation +2

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 Inference +3

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.

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

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

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

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 Explanation Explanation Generation

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.

Fairness Representation Learning

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

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

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

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 +2

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.

Data Augmentation Fairness

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 Graph Learning +3

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

1 code implementation20 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 +2

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

1 code implementation23 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 Recommendation Systems

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

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

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

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

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

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.

Clustering Link Prediction +2

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

Cannot find the paper you are looking for? You can Submit a new open access paper.