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In this survey we overview the definitions and methods for graph clustering, that is, finding
sets of “related” vertices in graphs. We review the many definitions for what is a cluster in a …

Recent works in self-supervised learning have advanced the state-of-the-art by relying on
the contrastive learning paradigm, which learns representations by pushing positive pairs, or …

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …

Semidefinite programs (SDPs) are a fundamental class of optimization problems with
important recent applications in approximation algorithms, quantum complexity, robust …

We develop new methods based on graph motifs for graph clustering, allowing more
efficient detection of communities within networks. We focus on triangles within graphs, but …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Users increasingly rely on the trustworthiness of the information exposed on Online Social
Networks (OSNs). In addition, OSN providers base their business models on the …

A large body of work has been devoted to defining and identifying clusters or communities in
social and information networks, ie, in graphs in which the nodes represent underlying …

Detecting clusters or communities in large real-world graphs such as large social or
information networks is a problem of considerable interest. In practice, one typically chooses …