no code implementations • 30 Jan 2024 • Yibo Li, Xiao Wang, Yujie Xing, Shaohua Fan, Ruijia Wang, Yaoqi Liu, Chuan Shi
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
1 code implementation • 23 Jan 2024 • Yanhu Mo, Xiao Wang, Shaohua Fan, Chuan Shi
How can we fix it and encourage the current GCL to learn better invariant representations?
1 code implementation • 30 Nov 2022 • Shaohua Fan, Shuyang Zhang, Xiao Wang, Chuan Shi
In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features.
1 code implementation • 28 Sep 2022 • Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang
However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists.
no code implementations • 19 Jan 2022 • Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang
Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.
1 code implementation • 20 Nov 2021 • Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.
no code implementations • 30 Nov 2020 • Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.
1 code implementation • 29 Jun 2020 • Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang
Most of existing clustering algorithms are proposed without considering the selection bias in data.