Graph clustering is a fundamental problem in machine learning. Deep learning methods
achieve the state-of-the-art results in recent years, but they still cannot work without …

Graph domain adaptation has recently enabled knowledge transfer across different graphs.
However, without the semantic information on target graphs, the performance on target …

Subgraph matching query is a classic problem in graph data management and has a variety
of real-world applications, such as discovering structures in biological or chemical networks …

In many real-world applications such as social network analysis, knowledge graph
discovery, biological network analytics, and so on, graph data management has become …

Due to powerful ability to learn representations from unlabeled graphs, graph contrastive
learning (GCL) has shown excellent performance in community detection tasks. Existing …

\Graph similarity computation is an essential task in many real-world graph-related
applications such as retrieving the similar drugs given a query chemical compound or …

Subgraph matching stands as a fundamental issue within the research realm of graph
analysis. In this paper, we investigate a novel combinatorial problem that encompasses both …