Details

Autor(en) / Beteiligte

• Chunaev, Petr

Titel

Community detection in node-attributed social networks: A survey

Ist Teil von

• Computer science review, 2020-08, Vol.37, p.100286, Article 100286

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social graph) with certain social connections (modeled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes (traditionally called node attributes), although the majority of real-world social networks provide additional actors’ information such as age, gender, interests, etc. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of the network (modeled already via a node-attributed graph) to yield more informative and qualitative community detection results. During the last decade many such methods based on different ideas and techniques have appeared. Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice. In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how the structure and the attributes are fused. We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their performance evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future.