X-Mark: a benchmark for node-attributed community discovery algorithms

Citraro S., Rossetti G.

Computer Science Applications  Node-attributed community discovery  Media Technology  Information Systems  Network models  Labeled community discovery  Human-Computer Interaction  Synthetic benchmarks  Communication 

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.

Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2

Publisher: Springer, Vienna

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