Contributo in atti di convegno, 2020, ENG, 10.1007/978-3-030-36687-2_12

Eva: attribute-aware network segmentation

Citraro S.; Rossetti G.

CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy

Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.

International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019

Keywords

Community discovery

CNR authors

Citraro Salvatore, Rossetti Giulio

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 415652

Year: 2020

Type: Contributo in atti di convegno

Creation: 2020-01-20 14:07:20.000

Last update: 2023-06-28 16:25:59.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:415652

DOI: 10.1007/978-3-030-36687-2_12

Scopus: 2-s2.0-85076677411

ISI Web of Science (WOS): 000843927300012