期刊
SOCIAL NETWORK ANALYSIS AND MINING
卷 11, 期 1, 页码 -出版社
SPRINGER WIEN
DOI: 10.1007/s13278-021-00823-2
关键词
Network models; Synthetic benchmarks; Labeled community discovery; Node-attributed community discovery
资金
- scheme 'INFRAIA-01-2018-2019: Research and Innovation action'' 'SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics' [871042]
The study introduces a new model X-Mark for generating synthetic node-attributed graphs with planted communities and compares attribute-aware algorithms, showing that X-Mark can illuminate differences between various algorithm families.
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.
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