Comparison of Graph Node Distances on Clustering Tasks
(2016) Lecture Notes in Computer Science — Vol. 9886, p. 192-201 (2016)
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- 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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Citations
Sommer, F., Fouss, F., & Saerens, M. (2016). Comparison of Graph Node Distances on Clustering Tasks. Lecture Notes in Computer Science, 9886, 192-201. https://doi.org/10.1007/978-3-319-44778-0_23 (Original work published 2016)