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07/19/2023
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Modeling customer shopping intentions is a crucial task for e-commerce, ...
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07/10/2023
Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective
Graph neural networks have shown great ability in representation (GNNs) ...
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06/21/2023
STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
Recommendation systems play a vital role in many online platforms, with ...
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06/19/2023
Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains. ...
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06/14/2023
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Graph Neural Networks (GNNs) have achieved great success in modeling gra...
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06/13/2023
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Imitation learning has achieved great success in many sequential decisio...
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04/03/2023
Counterfactual Learning on Graphs: A Survey
Graph-structured data are pervasive in the real-world such as social net...
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02/11/2023
Unnoticeable Backdoor Attacks on Graph Neural Networks
Graph Neural Networks (GNNs) have achieved promising results in various ...
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01/07/2023
Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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12/16/2022
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Graph serves as a powerful tool for modeling data that has an underlying...
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10/05/2022
Towards Prototype-Based Self-Explainable Graph Neural Network
Graph Neural Networks (GNNs) have shown great ability in modeling graph-...
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08/03/2022
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Link prediction is an important task that has wide applications in vario...
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06/10/2022
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
In recent years, graph neural networks (GNNs) have achieved state-of-the...
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06/07/2022
Decoupled Self-supervised Learning for Non-Homophilous Graphs
In this paper, we study the problem of conducting self-supervised learni...
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06/07/2022
Towards Bridging Algorithm and Theory for Unbiased Recommendation
This work studies the problem of learning unbiased algorithms from biase...
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05/27/2022
On Consistency in Graph Neural Network Interpretation
Uncovering rationales behind predictions of graph neural networks (GNNs)...
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04/18/2022
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent ...
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03/30/2022
Learning Fair Models without Sensitive Attributes: A Generative Approach
Most existing fair classifiers rely on sensitive attributes to achieve f...
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02/23/2022
Exploring Edge Disentanglement for Node Classification
Edges in real-world graphs are typically formed by a variety of factors ...
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01/01/2022
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Graph Neural Networks (GNNs) have shown their great ability in modeling ...
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10/15/2021
Label-Wise Message Passing Graph Neural Network on Heterophilic Graphs
Graph Neural Networks (GNNs) have achieved remarkable performance in mod...
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08/26/2021
Towards Self-Explainable Graph Neural Network
Graph Neural Networks (GNNs), which generalize the deep neural networks ...
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08/07/2021
Jointly Attacking Graph Neural Network and its Explanations
Graph Neural Networks (GNNs) have boosted the performance for many graph...
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06/08/2021
Labeled Data Generation with Inexact Supervision
The recent advanced deep learning techniques have shown the promising re...
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06/08/2021
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
Graph Neural Networks (GNNs) have achieved promising results for semi-su...
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05/27/2021
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
The world is increasingly urbanizing and the building industry accounts ...
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04/29/2021
You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features
Though machine learning models are achieving great success, ex-tensive s...
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03/16/2021
Semi-Supervised Graph-to-Graph Translation
Graph translation is very promising research direction and has a wide ra...
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03/16/2021
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
Node classification is an important research topic in graph learning. Gr...
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02/20/2021
Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis
A software vulnerability could be exploited without any visible symptoms...
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11/23/2020
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
Time series data is prevalent in a wide variety of real-world applicatio...
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09/03/2020
FairGNN: Eliminating the Discrimination in Graph Neural Networks with Limited Sensitive Attribute Information
Graph neural networks (GNNs) have shown great power in modeling graph st...
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09/01/2020
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
In recent years, the proliferation of so-called "fake news" has caused m...
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06/28/2020
Graph Convolutional Networks against Degree-Related Biases
In recent years, Graph Convolutional Networks (GCNs) show competitive pe...
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06/17/2020
Self-supervised Learning on Graphs: Deep Insights and New Direction
The success of deep learning notoriously requires larger amounts of cost...
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06/10/2020
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
With the rapid growth and prevalence of social network applications (App...
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05/20/2020
Graph Structure Learning for Robust Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools in representation learni...
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01/27/2020
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
Nowadays, Internet is a primary source of attaining health information. ...
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11/22/2019
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various dom...
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11/05/2019
Why X rather than Y? Explaining Neural Model' Predictions by Generating Intervention Counterfactual Samples
Even though the topic of explainable AI/ML is very popular in text and c...
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10/07/2019
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, ...
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09/14/2019
Node Injection Attacks on Graphs via Reinforcement Learning
Real-world graph applications, such as advertisements and product recomm...
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08/20/2019
Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning
Graph neural networks (GNNs) are widely used in many applications. Howev...
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07/06/2019
I Am Not What I Write: Privacy Preserving Text Representation Learning
Online users generate tremendous amounts of textual information by parti...
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06/10/2019
Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph ...
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04/30/2019
The Role of User Profile for Fake News Detection
Consuming news from social media is becoming increasingly popular. Socia...
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04/30/2019
Graph Convolutional Networks with EigenPooling
Graph neural networks, which generalize deep neural network models to gr...
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09/15/2017
Disentangled Variational Auto-Encoder for Semi-supervised Learning
In this paper, we develop a novel approach for semi-supervised VAE witho...
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09/14/2017
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
Most social media platforms are largely based on text, and users often w...
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08/07/2017
