Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis
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 fr...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9631295/ |
| _version_ | 1828113417618063360 |
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| author | Mohamed Hamza Ibrahim Rokia Missaoui Jean Vaillancourt |
| author_facet | Mohamed Hamza Ibrahim Rokia Missaoui Jean Vaillancourt |
| author_sort | Mohamed Hamza Ibrahim |
| collection | DOAJ |
| description | 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 <italic>Bi-face</italic> (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 with other traditional centrality measures. On the datasets tested, the computation of 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. |
| first_indexed | 2024-04-11T12:06:29Z |
| format | Article |
| id | doaj.art-bec0dd3bab344d348c6ab36ff1a05b99 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T12:06:29Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bec0dd3bab344d348c6ab36ff1a05b992022-12-22T04:24:43ZengIEEEIEEE Access2169-35362021-01-01915954915956510.1109/ACCESS.2021.31319879631295Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept AnalysisMohamed Hamza Ibrahim0https://orcid.org/0000-0002-0604-2709Rokia Missaoui1https://orcid.org/0000-0001-7410-4177Jean Vaillancourt2https://orcid.org/0000-0002-9236-7728Department of CS and Engineering, University of Quebec in Outaouais, Gatineau, CanadaDepartment of CS and Engineering, University of Quebec in Outaouais, Gatineau, CanadaDepartment of Decision Sciences, HEC Montréal, Montréal, CanadaIdentifying 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 <italic>Bi-face</italic> (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 with other traditional centrality measures. On the datasets tested, the computation of 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.https://ieeexplore.ieee.org/document/9631295/Formal concept analysistwo-mode networksinfluential nodecross-clique connectivity |
| spellingShingle | Mohamed Hamza Ibrahim Rokia Missaoui Jean Vaillancourt Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis IEEE Access Formal concept analysis two-mode networks influential node cross-clique connectivity |
| title | Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis |
| title_full | Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis |
| title_fullStr | Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis |
| title_full_unstemmed | Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis |
| title_short | Identifying Influential Nodes in Two-Mode Data Networks Using Formal Concept Analysis |
| title_sort | identifying influential nodes in two mode data networks using formal concept analysis |
| topic | Formal concept analysis two-mode networks influential node cross-clique connectivity |
| url | https://ieeexplore.ieee.org/document/9631295/ |
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