Pattern Discovery in Triadic Contexts

Missaoui, Rokia; Ruas, Pedro H. B.; Kwuida, Léonard; Song, Mark A. J.

doi:10.1007/978-3-030-57855-8_9

Cited by 4 publications

(2 citation statements)

References 11 publications

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Section: Discussionmentioning

confidence: 99%

“…We believe that the bi-face centrality measure proposed in this paper for two-mode networks can be adapted and applied to more complex networks such as multidimensional and multilayer ones [32], [33] in order to find relevant patterns, including social network communities [34]. Our next research steps will consist to first analyze triadic formal contexts (tridimensional datasets) to adapt our bi-face formulae to the new notions of extent-based and featurebased faces presented in [34]. Later on, we will analyze multilayer networks as interlinked two-mode data networks discussed in [33], to first express them as individual formal contexts, use the composition operator on contexts to form enriched ones with their corresponding concept lattices for which the bi-face centrality measure will finally be exploited and validated.…”

Section: Discussionmentioning

confidence: 99%

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Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis

2021

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Identifying important actors (or nodes) in a two-mode network is a crucial challenge in mining, analyzing, and interpreting real-world networks. While traditional bipartite centrality indices are often used to recognize key nodes that influence the network information flow, inaccurate results are frequently obtained in intricate situations such as massive networks with complex local structures or a lack of complete knowledge about the network topology and certain properties. In this paper, we introduce Bi-face (BF), a new bipartite centrality measurement for identifying important nodes in two-mode networks. Using the powerful mathematical formalism of Formal Concept Analysis, the BF measure exploits the faces of concept intents to detect nodes that have influential bicliques connectivity and are not located in irrelevant bridges. Unlike off-the shelf centrality indices, it quantifies how a node has a cohesive substructure influence on its neighbour nodes via bicliques while not being in network core-peripheral ones through its absence from non-influential bridges. In terms of identifying accurate node centrality, our experiments on a variety of real-world and synthetic networks show that BF outperforms several state-of-the art bipartite centrality measures, producing the most accurate Kendall coefficient. It provides unique node identification based on network topology. The findings also demonstrate that the presence of terminal nodes, influential bridges, and overlapping key bicliques impacts both the performance and behaviour of BF as well as its relationship to other traditional centrality measures. On the datasets tested, BF is at least twenty-three times faster than betweenness, eleven times faster than percolation, nine times faster than eigenvector, and ten times faster than closeness in terms of computation.INDEX TERMS Formal Concept Analysis, two-mode networks, influential node, cross-clique connectivity.

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Section: Discussionmentioning

confidence: 99%