Abstract
This work presents recent developments in graph node distances and tests them empirically on social network databases of various sizes and types. We compare two versions of a distance-based kernel k-means algorithm with the well-established Louvain method. The first version is a classic kernel k-means approach, the second version additionally makes use of node weights with the Sum-over-Forests density index. Both kernel k-means algorithms employ a variety of classic and modern distances. We compare the results of all three algorithms using statistical measures and an overall rank-comparison to ascertain their capabilities in community detection. Results show that two recently introduced distances outperform the others, on our tested datasets.
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Acknowledgements
We would like to thank our Master students Joëlle Van Damme and Augustin Collette for their valuable assistance in realizing this work. This work is supported in part by the FNRS through a PhD scholarship. This work was also partially supported by the Immediate and the Brufence projects funded by InnovIris (Brussels Region). We thank these institutions for giving us the opportunity to conduct both fundamental and applied research.
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Sommer, F., Fouss, F., Saerens, M. (2016). Comparison of Graph Node Distances on Clustering Tasks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_23
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