649 citations · 1.7k across the 57 of their papers we have counts for
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Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
Chaochao Yan, Sheng Wang, Jinyu Yang +2
Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previous work, we encode molecul…
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…