2 papers
cs.SI2021
Modeling Heterogeneous Edges to Represent Networks with Graph Auto-Encoder
Lu Wang, Yu Song, Hong Huang +3
In the real world, networks often contain multiple relationships among nodes, manifested as the heterogeneity of the edges in the networks. We convert the heterogeneous networks in…
cs.LG2019
Label-Aware Graph Convolutional Networks
Hao Chen, Yue Xu, Feiran Huang +5
Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly…