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

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