Eva: Attribute-Aware Network Segmentation

  • Salvatore Citraro
  • Giulio RossettiEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


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.



This work is partially supported by the European Community’s H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement 654024,, “SoBigData”.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.KDD LabISTI-CNRPisaItaly

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