@article {1358,
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {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.},
	doi = {https://doi.org/10.1007/s41109-020-00302-1},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1},
	author = {Salvatore Citraro and Giulio Rossetti}
}
