Journal article  Open Access

Identifying and exploiting homogeneous communities in labeled networks

Citraro S., Rossetti G.

Node label prediction  Labeled community discovery  Network homophily 

Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.

Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00302-1

Publisher: Springer international, Cham, Svizzera

Back to previous page
Projects (via OpenAIRE)

SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics

BibTeX entry
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	author = {Citraro S. and Rossetti G.},
	publisher = {Springer international, Cham, Svizzera},
	doi = {10.1007/s41109-020-00302-1},
	journal = {Applied network science},
	volume = {5},
	year = {2020}