Identifying and exploiting homogeneous communities in labeled networks
| Title | Identifying and exploiting homogeneous communities in labeled networks |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Citraro, S, Rossetti, G |
| Journal | Applied Network Science |
| Volume | 5 |
| Issue | 1 |
| Pagination | 1–20 |
| 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. |
| URL | https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1 |
| DOI | 10.1007/s41109-020-00302-1 |
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