Enumerating Maximal Isolated Cliques Based on Vertex-Dependent Connection Lower Bound

Okubo, Yoshiaki; Haraguchi, Masanobu; Tomita, Eiji

doi:10.1007/978-3-319-41920-6_45

Cited by 2 publications

(1 citation statement)

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“…Therefore, it is worth seeking additional constraints that would target the desired solutions more tightly. The authors have investigated a reasonable constraint based on the notion of variable-j-coreness [19] that considers the degree of isolation of cliques, and are currently extending it. Based on this work, we expect that the efficiency of our algorithm can be improved further, becoming practical even for huge networks of over a million vertices.…”

Section: Resultsmentioning

confidence: 99%

A fast and complete algorithm for enumerating pseudo-cliques in large graphs

Zhai

Haraguchi

Okubo

et al. 2016

Int J Data Sci Anal

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This paper discusses a complete and efficient algorithm for enumerating densely connected k-Plexes in networks. A k-Plex is a kind of pseudo-clique which imposes a disconnection upper bound (DUB) involving a parameter k for each constituent vertex. However, because the parameter value is usually set independently of the sizes of the targeted pseudo-cliques, we often obtain k-Plexes that are not densely connected. To overcome this drawback, we introduce another constraint, the connection lower bound (CLB), which involves a parameter j. Using the CLB, we can enjoy monotonic j-core operations and can design an efficient depth-first algorithm, which can exclude both search branches that generate duplicate search nodes and "hopeless" nodes that yield no targets satisfying both DUB and CLB. Our experimental results show that the algorithm can be a useful tool for detecting densely connected pseudo-cliques in large networks, including an example with over 800, 000 vertices. Keywords Pseudo-clique • k-Plex • j-Core • j-Cored maximal connected k-Plex • Enumeration algorithm This paper is an extended version of the PAKDD 2016 Long Presentation Paper, "A Fast and Complete Enumeration of Pseudo-Cliques for Large Graphs" [30].

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Section: Resultsmentioning

confidence: 99%