Abstract
The weight-based fusion model (WBFM) is one of the simplest and most efficient models for community detection (CD) in node-attributed social networks (ASNs) which contain both links between social actors (aka structure) and actors’ features (aka attributes). Although WBFM is widely used, it has a logical gap as we show here. Namely, the gap stems from the discrepancy between the so-called Composite Modularity that is usually optimized within WBFM and the measures used for CD quality evaluation. The discrepancy may cause the misinterpretation of CD results and difficulties with the parameter tuning within WBFM. To fulfil the gap, we theoretically study how Composite Modularity is related to the CD quality measures. This study further yields a pioneering non-manual parameter tuning scheme that provides the equal impact of structure and attributes on the CD results. Experiments with synthetic and real-world ASNs show that our conclusions help to reasonably interpret the CD results and that our tuning scheme is very accurate.
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Notes
- 1.
An edge weight may be zero and this indicates that there is no social connection.
- 2.
If one deals with nominal or textual attributes, it is common to use one-hot encoding or embeddings to obtain their numerical representation.
- 3.
Communities may be overlapping if necessary but here we focus on disjoint ones.
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This research was financially supported by the Russian Science Foundation, Agreement 19-71-10078.
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Chunaev, P., Gradov, T., Bochenina, K. (2021). Composite Modularity and Parameter Tuning in the Weight-Based Fusion Model for Community Detection in Node-Attributed Social Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_9
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