Extending Proximity Measures to Attributed Networks for Community Detection
Rinat Aynulin
Moscow Institute of Physics and Technology
9 Institutskii per.
Dolgoprudny, 141700, Russia
and
Kotel’nikov Institute of Radio-engineering and Electronics of the
Russian Academy of Sciences
11 Mokhovaya str.
Moscow, 125009, Russia
Pavel Chebotarev
Trapeznikov Institute of Control Sciences of the
Russian Academy of Sciences
65 Profsoyuznaya str.
Moscow, 117997, Russia
Abstract
Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are numerically studied in the context of community detection in real-world networks.
Keywords: attributed networks; community detection; proximity measure; kernel on graph
Cite this publication as:
R. Aynulin and P. Chebotarev, "Extending Proximity Measures to Attributed Networks for Community Detection," Complex Systems, 30(4), 2021 pp. 441–455.
https://doi.org/10.25088/ComplexSystems.30.4.441
