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Journal ArticleDOI

On cliques in graphs

J. W. Moon, +1 more
- 01 Mar 1965 - 
- Vol. 3, Iss: 1, pp 23-28
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TLDR
In this article, the maximum number of cliques possible in a graph with n nodes is determined and bounds are obtained for the number of different sizes of clique possible in such a graph.
Abstract
A clique is a maximal complete subgraph of a graph. The maximum number of cliques possible in a graph withn nodes is determined. Also, bounds are obtained for the number of different sizes of cliques possible in such a graph.

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Citations
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Journal ArticleDOI

On the number of minimal separators in graphs

TL;DR: The largest number of minimal separators a graph on n vertices can have is considered and it is proved that this number is in ω(1.4457n), improving on the previous best lower bound of Ω(3n/3)⊆ω (1.4422n), which gives an improved lower bound on the number of potential maximal cliques in a graph.
Journal ArticleDOI

All roads lead to Rome---New search methods for the optimal triangulation problem

TL;DR: This paper derives a new simple method based on the Bron-Kerbosch algorithm that compares favourably to Stix' approach and is generic in the sense that it can be used with other algorithms than just Bron-kerboschy, and made possible to evaluate the quality of heuristics precisely.
Dissertation

Independence Systems and Stable Set Relaxations

TL;DR: In this article, a Rice University thesis/dissertation was published as a paper entitled "Rice University thesis and dissertation: A.handle.net/1911/22172".
Journal ArticleDOI

On the number of k-dominating independent sets

TL;DR: In this paper, it was shown that the maximum number of k-dominating independent sets in n-vertex graphs is between ck·22kn and ck′·2k+1n if k≥2.
Posted ContentDOI

Protein complex prediction for large protein protein interaction networks with the Core&Peel Method

TL;DR: It is demonstrated that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions, and is also empirically efficient attaining the second best running time over large networks among the tested algorithms.
References
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Journal ArticleDOI

On the theory of graphs

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