Eva: attribute-aware network segmentation
Citraro S., Rossetti G.
Computer Science - Social and Information Networks FOS: Computer and information sciences Physics - Physics and Society Social and Information Networks (cs.SI) community discovery FOS: Physical sciences Community discovery 68R10 Physics and Society (physics.soc-ph)
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, Lisbon, Portugal, 10-12/12/2019
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@inproceedings{oai:it.cnr:prodotti:415652,
title = {Eva: attribute-aware network segmentation},
author = {Citraro S. and Rossetti G.},
doi = {10.1007/978-3-030-36687-2_12 and 10.48550/arxiv.1910.06599},
booktitle = {International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019},
year = {2020}
}
0000-0002-5021-4790ISTI Repository
DOI
10.1007/978-3-030-36687-2_12
10.48550/arxiv.1910.06599
