649 citations · 2.1k across the 52 of their papers we have counts for
6 papers · 1 filter
Graph Convolutional Networks for Temporal Action Localization
Runhao Zeng, Wenbing Huang, Mingkui Tan +4
Most state-of-the-art action localization systems process each action proposal individually, without explicitly exploiting their relations during learning. However, the relations b…
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
Heng Chang, Yu Rong, Tingyang Xu +5
With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central proble…
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong, Wenbing Huang, Tingyang Xu +1
\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting wea…
Unsupervised Adversarial Graph Alignment with Graph Embedding
Chaoqi Chen, Weiping Xie, Tingyang Xu +5
Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node corresponden…
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Chaoyang He, Tian Xie, Yu Rong +4
Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more…
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
Jia Li, Yu Rong, Hong Cheng +3
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph u…