2020
Conference article  Open Access

Eva: Attribute-Aware Network Segmentation

Citraro S., Rossetti G.

Computer Science - Social and Information Networks  Physics - Physics and Society  community discovery  68R10 

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.

Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbona, 10-12/12/2019

Publisher: Springer, Berlin , Germania


Citations

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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:415652,
	title = {Eva: Attribute-Aware Network Segmentation},
	author = {Citraro S. and Rossetti G.},
	publisher = {Springer, Berlin , Germania},
	doi = {10.1007/978-3-030-36687-2_12},
	booktitle = {International Conference on Complex Networks and their Applications, pp. 141–151, Lisbona, 10-12/12/2019},
	year = {2020}
}