Articolo in rivista, 2021, ENG, 10.1007/s13278-021-00823-2
Citraro S.; Rossetti G.
Department of Computer Science, University of Pisa e CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.
Social Network Analysis and Mining 11
Labeled community discovery, Network models, Node-attributed community discovery, Synthetic benchmarks
Citraro Salvatore, Rossetti Giulio
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 461038
Year: 2021
Type: Articolo in rivista
Creation: 2021-12-16 14:13:40.000
Last update: 2021-12-20 15:32:50.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/s13278-021-00823-2
URL: https://link.springer.com/article/10.1007%2Fs13278-021-00823-2
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:461038
DOI: 10.1007/s13278-021-00823-2
Scopus: 2-s2.0-85117328317
ISI Web of Science (WOS): 000707589700001