b'Jundong Li'

research

∙ 08/28/2023

Fair Few-shot Learning with Auxiliary Sets

Recently, there has been a growing interest in developing machine learni...

0 Song Wang, et al. ∙

research

∙ 08/18/2023

GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction

Self-supervised learning with masked autoencoders has recently gained po...

0 Yucheng Shi, et al. ∙

research

∙ 07/22/2023

Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attribu...

0 Qiaoyu Tan, et al. ∙

research

∙ 07/17/2023

Learning for Counterfactual Fairness from Observational Data

Fairness-aware machine learning has attracted a surge of attention in ma...

0 Jing Ma, et al. ∙

research

∙ 06/05/2023

Path-Specific Counterfactual Fairness for Recommender Systems

Recommender systems (RSs) have become an indispensable part of online pl...

0 Yaochen Zhu, et al. ∙

research

∙ 05/02/2023

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

In recent years, neural models have been repeatedly touted to exhibit st...

0 Yushun Dong, et al. ∙

research

∙ 01/03/2023

RELIANT: Fair Knowledge Distillation for Graph Neural Networks

Graph Neural Networks (GNNs) have shown satisfying performance on variou...

0 Yushun Dong, et al. ∙

research

∙ 01/03/2023

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

In the era of information overload, recommender systems (RSs) have becom...

0 Yaochen Zhu, et al. ∙

research

∙ 11/25/2022

Interpreting Unfairness in Graph Neural Networks via Training Node Attribution

Graph Neural Networks (GNNs) have emerged as the leading paradigm for so...

0 Yushun Dong, et al. ∙

research

∙ 10/16/2022

CLEAR: Generative Counterfactual Explanations on Graphs

Counterfactual explanations promote explainability in machine learning m...

0 Jing Ma, et al. ∙

research

∙ 09/30/2022

Contrastive Graph Few-Shot Learning

Prevailing deep graph learning models often suffer from label sparsity i...

0 Chunhui Zhang, et al. ∙

research

∙ 08/16/2022

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

Knowledge Graph Embeddings (KGE) aim to map entities and relations to lo...

0 Zhaoxuan Tan, et al. ∙

research

∙ 07/07/2022

Learning Causal Effects on Hypergraphs

Hypergraphs provide an effective abstraction for modeling multi-way grou...

15 Jing Ma, et al. ∙

research

∙ 06/24/2022

On Structural Explanation of Bias in Graph Neural Networks

Graph Neural Networks (GNNs) have shown satisfying performance in variou...

0 Yushun Dong, et al. ∙

research

∙ 06/09/2022

TwiBot-22: Towards Graph-Based Twitter Bot Detection

Twitter bot detection has become an increasingly important task to comba...

0 Shangbin Feng, et al. ∙

research

∙ 06/07/2022

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Graph Neural Networks (GNNs) have shown great power in learning node rep...

0 Yu Wang, et al. ∙

research

∙ 05/20/2022

Towards Explanation for Unsupervised Graph-Level Representation Learning

Due to the superior performance of Graph Neural Networks (GNNs) in vario...

0 Qinghua Zheng, et al. ∙

research

∙ 04/24/2022

Empowering Next POI Recommendation with Multi-Relational Modeling

With the wide adoption of mobile devices and web applications, location-...

0 Zheng Huang, et al. ∙

research

∙ 04/08/2022

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

Political perspective detection has become an increasingly important tas...

0 Wenqian Zhang, et al. ∙

research

∙ 03/29/2022

Self-Supervised Learning for Recommender Systems: A Survey

Neural architecture-based recommender systems have achieved tremendous s...

0 Junliang Yu, et al. ∙

research

∙ 01/21/2022

Robust Unsupervised Graph Representation Learning via Mutual Information Maximization

Recent studies have shown that GNNs are vulnerable to adversarial attack...

11 Jihong Wang, et al. ∙

research

∙ 01/10/2022

Learning Fair Node Representations with Graph Counterfactual Fairness

Fair machine learning aims to mitigate the biases of model predictions a...

69 Jing Ma, et al. ∙

research

∙ 10/26/2021

Unbiased Graph Embedding with Biased Graph Observations

Graph embedding techniques have been increasingly employed in real-world...

0 Nan Wang, et al. ∙

research

∙ 09/09/2021

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

With the prevalence of social media, there has recently been a prolifera...

0 Junwei Zhang, et al. ∙

research

∙ 08/11/2021

EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

Graph Neural Networks (GNNs) have recently demonstrated superior capabil...

0 Yushun Dong, et al. ∙

research

∙ 05/29/2021

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

To mitigate the spread of COVID-19 pandemic, decision-makers and public ...

0 Jing Ma, et al. ∙

research

∙ 04/26/2021

Graph Neural Networks with Adaptive Frequency Response Filter

Graph Neural Networks have recently become a prevailing paradigm for var...

17 Yushun Dong, et al. ∙

research

∙ 02/27/2021

Automated Generation of Interorganizational Disaster Response Networks through Information Extraction

When a disaster occurs, maintaining and restoring community lifelines su...

0 Yitong Li, et al. ∙

research

∙ 01/16/2021

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

Social relations are often used to improve recommendation quality when u...

0 Junliang Yu, et al. ∙

research

∙ 10/20/2020

Line Graph Neural Networks for Link Prediction

We consider the graph link prediction task, which is a classic graph ana...

18 Lei Cai, et al. ∙

research

∙ 04/22/2020

Scalable Attack on Graph Data by Injecting Vicious Nodes

Recent studies have shown that graph convolution networks (GCNs) are vul...

0 Jihong Wang, et al. ∙

research

∙ 04/05/2020

Enhance Social Recommendation with Adversarial Graph Convolutional Networks

Recent reports from industry show that social recommender systems consis...

0 Junliang Yu, et al. ∙

research

∙ 09/08/2019

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Most of the recent studies of social recommendation assume that people s...

0 Junliang Yu, et al. ∙

research

∙ 08/19/2019

Graph Neural Networks with High-order Feature Interactions

Network representation learning, a fundamental research problem which ai...

0 Kaize Ding, et al. ∙

research

∙ 08/11/2019

SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

Anomaly detection aims to distinguish observations that are rare and dif...

0 Yuening Li, et al. ∙

research

∙ 08/11/2019

Deep Structured Cross-Modal Anomaly Detection

Anomaly detection is a fundamental problem in data mining field with man...

8 Yuening Li, et al. ∙

research

∙ 07/17/2019

Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Machine learning models are currently being deployed in a variety of rea...

0 Yuxin Ma, et al. ∙

research

∙ 06/08/2019

Learning Individual Treatment Effects from Networked Observational Data

With convenient access to observational data, learning individual causal...

0 Ruocheng Guo, et al. ∙

research

∙ 11/25/2018

Online Newton Step Algorithm with Estimated Gradient

Online learning with limited information feedback (bandit) tries to solv...

0 Binbin Liu, et al. ∙

research

∙ 09/25/2018

A Survey of Learning Causality with Data: Problems and Methods

The era of big data provides researchers with convenient access to copio...

0 Ruocheng Guo, et al. ∙

research

∙ 08/26/2018

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

As opposed to manual feature engineering which is tedious and difficult ...

0 Jundong Li, et al. ∙

research

∙ 06/06/2017

Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low...

0 Jundong Li, et al. ∙

Jundong Li

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