Contributo in atti di convegno, 2020, ENG, 10.1007/978-3-030-36687-2_12
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
Citraro Salvatore, Rossetti Giulio
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
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-030-36687-2_12
URL: https://link.springer.com/chapter/10.1007%2F978-3-030-36687-2_12
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