The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the
associated original graphs, and many graph properties are known to be inherited by …

Graphs have been widely used to represent complex data in many applications, such as e-
commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data …

Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …

Combinatorial optimization algorithms for graph problems are usually designed afresh for
each new problem with careful attention by an expert to the problem structure. In this work …

Subgraph learning has received considerable attention in its capacity of interpreting
important structural information for predictions. Existing subgraph learning usually exploits …

Graph matching (GM) aims at discovering node matching between graphs, by maximizing
the node-and edge-wise affinities between the matched elements. As an NP-hard problem …

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data
have been widely used in various domains, ranging from drug discovery to recommender …

Graph neural networks (GNNs) and message passing neural networks (MPNNs) have been
proven to be expressive for subgraph structures in many applications. Some applications in …

Deep learning methods have demonstrated promising performance on the NP-hard Graph
Matching (GM) problems. However, the state-of-the-art methods usually require the ground …