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.

翻译：在双模式网络中,确定重要行为者(或节点)往往仍然是采矿、分析和解释现实世界网络的重大挑战。虽然传统的双方核心指数常常被用来识别影响网络信息流动的关键节点,但往往在复杂的局势中产生不良结果,如拥有复杂的地方结构的大型网络,或缺乏对网络地形和某些特性的完整知识。在本文中,我们引入双面(Bi-face),这是用于确定双模式网络中重要节点的一个新的双方核心衡量标准。利用正规概念分析的强大数学形式主义,BF衡量利用概念意图的面孔来识别具有有影响力的双端连接和不相关桥梁的节点。与架子中心指数不同的是,它量化了无主节点如何通过双层对邻居的内聚层影响,而没有在网络核心外缘,因为没有非通桥。我们在一些真实世界和合成网络上的实验显示BF在现有的突出双端核心措施上的效率,例如相互之间。