Abstract
We present a pretopological based approach to extract ego-centered communities. Classical methods often consider only one structural feature of the network, whereas pretopology enables to do multi-criteria analysis. Our approach consists in learning a logical combination of network’s descriptors to define a pretopological space. Ego-centered communities are extracted by computing the elementary closure of each node. The quality of such communities is evaluated against the ground truth communities. We show the benefits of our method by comparing it to others on both real and synthetic networks.
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Let us notice that in practice, few examples are sufficient for driving efficiently the learning process.
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Caillaut, G., Cleuziou, G., Dugué, N. (2019). Learning Pretopological Spaces to Extract Ego-Centered Communities. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_38
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