Jundong Li

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Arizona State University ∙ University of Virginia ∙ Texas A&M University

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

Social relations are often used to improve recommendation quality when u...
01/16/2021 ∙ by Junliang Yu, et al. ∙ 0 ∙ share

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Line Graph Neural Networks for Link Prediction

We consider the graph link prediction task, which is a classic graph ana...
10/20/2020 ∙ by Lei Cai, et al. ∙ 18 ∙ share

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Scalable Attack on Graph Data by Injecting Vicious Nodes

Recent studies have shown that graph convolution networks (GCNs) are vul...
04/22/2020 ∙ by Jihong Wang, et al. ∙ 0 ∙ share

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Enhance Social Recommendation with Adversarial Graph Convolutional Networks

Recent reports from industry show that social recommender systems consis...
04/05/2020 ∙ by Junliang Yu, et al. ∙ 0 ∙ share

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Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Most of the recent studies of social recommendation assume that people s...
09/08/2019 ∙ by Junliang Yu, et al. ∙ 0 ∙ share

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Graph Neural Networks with High-order Feature Interactions

Network representation learning, a fundamental research problem which ai...
08/19/2019 ∙ by Kaize Ding, et al. ∙ 0 ∙ share

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SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

Anomaly detection aims to distinguish observations that are rare and dif...
08/11/2019 ∙ by Yuening Li, et al. ∙ 0 ∙ share

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Deep Structured Cross-Modal Anomaly Detection

Anomaly detection is a fundamental problem in data mining field with man...
08/11/2019 ∙ by Yuening Li, et al. ∙ 8 ∙ share

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Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Machine learning models are currently being deployed in a variety of rea...
07/17/2019 ∙ by Yuxin Ma, et al. ∙ 0 ∙ share

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Learning Individual Treatment Effects from Networked Observational Data

With convenient access to observational data, learning individual causal...
06/08/2019 ∙ by Ruocheng Guo, et al. ∙ 0 ∙ share

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Online Newton Step Algorithm with Estimated Gradient

Online learning with limited information feedback (bandit) tries to solv...
11/25/2018 ∙ by Binbin Liu, et al. ∙ 0 ∙ share

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A Survey of Learning Causality with Data: Problems and Methods

The era of big data provides researchers with convenient access to copio...
09/25/2018 ∙ by Ruocheng Guo, et al. ∙ 0 ∙ share

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

As opposed to manual feature engineering which is tedious and difficult ...
08/26/2018 ∙ by Jundong Li, et al. ∙ 0 ∙ share

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Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low...
06/06/2017 ∙ by Jundong Li, et al. ∙ 0 ∙ share

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Jundong Li

Featured Co-authors

Jie Wang

Jiliang Tang

Xia Hu

Shuiwang Ji

Xiangliang Zhang

Zi Huang

Yi Chang

Huan Liu

Hongzhi Yin

Xiao Huang

Mengnan Du

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

Line Graph Neural Networks for Link Prediction

Scalable Attack on Graph Data by Injecting Vicious Nodes

Enhance Social Recommendation with Adversarial Graph Convolutional Networks

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Graph Neural Networks with High-order Feature Interactions

SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

Deep Structured Cross-Modal Anomaly Detection

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Learning Individual Treatment Effects from Networked Observational Data

Online Newton Step Algorithm with Estimated Gradient

A Survey of Learning Causality with Data: Problems and Methods

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

Attributed Network Embedding for Learning in a Dynamic Environment