Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis
This work addresses a crucial challenge in network analysis for researchers and practitioners dealing with real-world two-mode data, but it is incremental as it builds on existing centrality methods with a novel adaptation.
The paper tackles the problem of identifying influential nodes in two-mode networks, where traditional bipartite centrality measures often fail in complex scenarios, by introducing a new centrality measure called Bi-face (BF) that uses Formal Concept Analysis to assess cohesive-substructure influence via bicliques, and experiments show BF outperforms existing measures like betweenness and eigenvector.
Identifying important actors (or nodes) in a two-mode network often remains 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, they frequently produce poor results 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 identify 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. Our experiments on several real-world and synthetic networks show the efficiency of BF over existing prominent bipartite centrality measures such as betweenness, closeness, eigenvector, and vote-rank among others.