Bringing a Feature Selection Metric from Machine Learning to Complex Networks
Introduced in the context of machine learning, the Feature F-measure is a statistical feature selection metric without parameters that allows to describe classes through a set of salient features. It was shown efficient for classification, cluster labeling and clustering model quality measurement. In this paper, we introduce the Node F-measure, its transposition in the context of networks, where it can by analogy be applied to detect salient nodes in communities. This approach benefits from the parameter-free system of Feature F-Measure, its low computational complexity and its well-evaluated performance. Interestingly, we show that in addition to these properties, Node F-measure is correlated with certain centrality measures, and with measures designed to characterize the community roles of nodes. We also observe that the usual community roles measures are strongly dependent from the size of the communities whereas the ones we propose are by definition linked to the density of the community. This hence makes their results comparable from one network to another. Finally, the parameter-free selection process applied to nodes allows for a universal system, contrary to the thresholds previously defined empirically for the establishment of community roles. These results may have applications regarding leadership in scientific communities or when considering temporal monitoring of communities.
- 7.Kunegis, J.: Konect: the koblenz network collection. In: WWW, pp. 1343–1350 (2013)Google Scholar
- 8.Lamirel, J.C., Cuxac, P., Chivukula, A., Hajlaoui, K.: Optimizing text classification through efficient feature selection based on quality metric. J. I. IS 45(3), 379–396 (2015)Google Scholar
- 9.Lamirel, J.C., Dugué, N., Cuxac, P.: New efficient clustering quality indexes. In: IJCNN (2016)Google Scholar
- 13.Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. 69(2), 026113 (2004)Google Scholar