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08/28/2023
Fair Few-shot Learning with Auxiliary Sets
Recently, there has been a growing interest in developing machine learni...
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08/18/2023
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction
Self-supervised learning with masked autoencoders has recently gained po...
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07/22/2023
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attribu...
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07/17/2023
Learning for Counterfactual Fairness from Observational Data
Fairness-aware machine learning has attracted a surge of attention in ma...
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06/05/2023
Path-Specific Counterfactual Fairness for Recommender Systems
Recommender systems (RSs) have become an indispensable part of online pl...
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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...
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01/03/2023
RELIANT: Fair Knowledge Distillation for Graph Neural Networks
Graph Neural Networks (GNNs) have shown satisfying performance on variou...
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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...
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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...
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10/16/2022
CLEAR: Generative Counterfactual Explanations on Graphs
Counterfactual explanations promote explainability in machine learning m...
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09/30/2022
Contrastive Graph Few-Shot Learning
Prevailing deep graph learning models often suffer from label sparsity i...
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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...
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07/07/2022
Learning Causal Effects on Hypergraphs
Hypergraphs provide an effective abstraction for modeling multi-way grou...
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06/24/2022
On Structural Explanation of Bias in Graph Neural Networks
Graph Neural Networks (GNNs) have shown satisfying performance in variou...
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06/09/2022
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to comba...
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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...
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05/20/2022
Towards Explanation for Unsupervised Graph-Level Representation Learning
Due to the superior performance of Graph Neural Networks (GNNs) in vario...
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04/24/2022
Empowering Next POI Recommendation with Multi-Relational Modeling
With the wide adoption of mobile devices and web applications, location-...
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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...
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03/29/2022
Self-Supervised Learning for Recommender Systems: A Survey
Neural architecture-based recommender systems have achieved tremendous s...
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01/21/2022
Robust Unsupervised Graph Representation Learning via Mutual Information Maximization
Recent studies have shown that GNNs are vulnerable to adversarial attack...
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01/10/2022
Learning Fair Node Representations with Graph Counterfactual Fairness
Fair machine learning aims to mitigate the biases of model predictions a...
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10/26/2021
Unbiased Graph Embedding with Biased Graph Observations
Graph embedding techniques have been increasingly employed in real-world...
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09/09/2021
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
With the prevalence of social media, there has recently been a prolifera...
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08/11/2021
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
Graph Neural Networks (GNNs) have recently demonstrated superior capabil...
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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 ...
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04/26/2021
Graph Neural Networks with Adaptive Frequency Response Filter
Graph Neural Networks have recently become a prevailing paradigm for var...
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02/27/2021
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
When a disaster occurs, maintaining and restoring community lifelines su...
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01/16/2021
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
Social relations are often used to improve recommendation quality when u...
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10/20/2020
Line Graph Neural Networks for Link Prediction
We consider the graph link prediction task, which is a classic graph ana...
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04/22/2020
Scalable Attack on Graph Data by Injecting Vicious Nodes
Recent studies have shown that graph convolution networks (GCNs) are vul...
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04/05/2020
Enhance Social Recommendation with Adversarial Graph Convolutional Networks
Recent reports from industry show that social recommender systems consis...
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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...
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08/19/2019
Graph Neural Networks with High-order Feature Interactions
Network representation learning, a fundamental research problem which ai...
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08/11/2019
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Anomaly detection aims to distinguish observations that are rare and dif...
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08/11/2019
Deep Structured Cross-Modal Anomaly Detection
Anomaly detection is a fundamental problem in data mining field with man...
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07/17/2019
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Machine learning models are currently being deployed in a variety of rea...
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06/08/2019
Learning Individual Treatment Effects from Networked Observational Data
With convenient access to observational data, learning individual causal...
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11/25/2018
Online Newton Step Algorithm with Estimated Gradient
Online learning with limited information feedback (bandit) tries to solv...
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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...
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08/26/2018
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
As opposed to manual feature engineering which is tedious and difficult ...
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06/06/2017