Search Results for author: Jundong Li

Found 39 papers, 18 papers with code

Towards Explanation for Unsupervised Graph-Level Representation Learning

no code implementations20 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.

Classification Few-Shot Learning +1

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

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

1 code implementation8 Apr 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 this survey, a timely and systematical review of the research efforts on self-supervised recommendation (SSR) is presented.

Recommendation Systems Self-Supervised Learning

Few-Shot Learning on Graphs: A Survey

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

Robust Unsupervised Graph Representation Learning via Mutual Information Maximization

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

In particular, to quantify the robustness of GNNs without label information, we propose a robustness measure, named graph representation robustness (GRR), to evaluate the mutual information between adversarially perturbed node representations and the original graph.

Adversarial Attack Graph Representation Learning +1

Learning Fair Node Representations with Graph Counterfactual Fairness

no code implementations10 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

1 code implementation29 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

Weakly-supervised Graph Meta-learning for 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 +1

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.

Classification General Classification +3

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

no code implementations22 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

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.

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

1 code implementation 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).

online learning

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.

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.

Link Prediction Network Embedding +1

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

1 code implementation29 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.