72 citations · 220 across the 23 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 2 cited
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
Zhabiz Gharibshah, Xingquan Zhu
In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entiti…
cs.NE2020★ 14 cited
Evolutionary Architecture Search for Graph Neural Networks
Min Shi, David A. Wilson, Xingquan Zhu +4
Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen…
cs.SI2020★ 6 cited
Topology and Content Co-Alignment Graph Convolutional Learning
Min Shi, Yufei Tang, Xingquan Zhu
In traditional Graph Neural Networks (GNN), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learni…