Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

This handbook provides a comprehensive and unified account of the main research
developments in cluster analysis. Written by active, distinguished researchers in this area …

The classification problem is closely related to the clustering problem discussed in Chaps. 6
and 7. While the clustering problem is that of determining similar groups of data points, the …

Spectral clustering converts the data clustering problem to the graph cut problem. It is based
on graph theory. Due to the reliable theoretical basis and good clustering performance …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

This article studies self-supervised graph representation learning, which is critical to various
tasks, such as protein property prediction. Existing methods typically aggregate …

Genome-scale metabolic networks (GSMs) are fundamental systems biology
representations of a cell's entire set of stoichiometrically balanced reactions. However, such …

Community search enables personalized community discovery and has wide applications in
large real-world graphs. While community search has been extensively studied for …

Graph clustering has generally concerned itself with clustering undirected graphs; however
the graphs from a number of important domains are essentially directed, eg networks of web …

Communities are prevalent in social networks, knowledge graphs, and biological networks.
Recently, the topic of community search (CS), extracting a dense subgraph containing a …