114 citations · 286 across the 20 of their papers we have counts for
5 papers · 1 filter
Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification
Xiao Shen, Mengqiu Shao, Shirui Pan +2
Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from di…
Neighbor Contrastive Learning on Learnable Graph Augmentation
Xiao Shen, Dewang Sun, Shirui Pan +2
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt h…
Adversarial Deep Network Embedding for Cross-network Node Classification
Xiao Shen, Quanyu Dai, Fu-lai Chung +2
In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network,…
Deep Network Embedding for Graph Representation Learning in Signed Networks
Xiao Shen, Fu-Lai Chung
Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dime…
Network Together: Node Classification via Cross network Deep Network Embedding
Xiao Shen, Quanyu Dai, Sitong Mao +2
Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network…