Xingquan Zhu

Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning

2019

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail …

Unsupervised Domain Adaptive Graph Convolutional Networks

2020

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the …