  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.
