579 citations · 3k across the 98 of their papers we have counts for
13 papers · 1 filter
Robust Collaborative Filtering to Popularity Distribution Shift
An Zhang, Wenchang Ma, Jingnan Zheng +2
In leading collaborative filtering (CF) models, representations of users and items are prone to learn popularity bias in the training data as shortcuts. The popularity shortcut tri…
Discovering Dynamic Causal Space for DAG Structure Learning
Fangfu Liu, Wenchang Ma, An Zhang +3
Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine lear…
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
An Zhang, Fangfu Liu, Wenchang Ma +3
Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data b…
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li, Xiang Wang, An zhang +3
Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic infor…
Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang +3
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are…
Reinforced Causal Explainer for Graph Neural Networks
Xiang Wang, Yingxin Wu, An Zhang +3
Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent tec…
Training Free Graph Neural Networks for Graph Matching
Zhiyuan Liu, Yixin Cao, Fuli Feng +4
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution wit…
Deconfounding to Explanation Evaluation in Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +4
Explainability of graph neural networks (GNNs) aims to answer "Why the GNN made a certain prediction?", which is crucial to interpret the model prediction. The feature attribution…
Discovering Invariant Rationales for Graph Neural Networks
Ying-Xin Wu, Xiang Wang, An Zhang +2
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction. Unfortunately,…
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui, Xiang Wang, Jiancan Wu +3
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mos…
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
Yunshan Ma, Yujuan Ding, Xun Yang +5
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR…
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media
Yujuan Ding, Yunshan Ma, Lizi Liao +2
Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devote…
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
Fuli Feng, Weiran Huang, Xiangnan He +3
Graph Convolutional Network (GCN) is an emerging technique for information retrieval (IR) applications. While GCN assumes the homophily property of a graph, real-world graphs are n…