Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph Neural Networks (GNNs) have shown superior performance in modeling graph data.
Existing studies have shown that a lot of GNNs perform well on homophilic graphs while …

Abstract Graph Neural Networks (GNNs) have shown advantages in various graph-based
applications. Most existing GNNs assume strong homophily of graph structure and apply …

To learn the embedding representation of graph structure data corrupted by noise and
outliers, existing graph structure learning networks usually follow the two-step paradigm, ie …

In the toxicological testing of new small-molecule compounds, it is desirable to establish in
silico test methods to predict toxicity instead of relying on animal testing. Since quantitative …

Network entities have important asset mapping, vulnerability, and service delivery
applications. In cyberspace, where the network structure is complex and the number of …

In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep
learning framework and incorporating more characteristics of Bitcoin transactions. We …

Abstract Knowledge Graph (KG) completion through reasoning over KGs is one of the most
concerned issue in research around knowledge graphs. Current work focuses more on …