Algorithms for Finding Maximal and Maximum Cliques: A Survey

2018 ◽  
pp. 745-754 ◽  
Author(s):  
Faten Fakhfakh ◽  
Mohamed Tounsi ◽  
Mohamed Mosbah ◽  
Ahmed Hadj Kacem
Keyword(s):  
Maximum Cliques

Enumerating maximum cliques in massive graphs

2020 ◽  
pp. 1-1
Author(s):  
Can Lu ◽  
Jeffrey Xu Yu ◽  
Hao Wei ◽  
Yikai Zhang
Keyword(s):  
Maximum Cliques ◽  
Massive Graphs

scholarly journals Finding Maximum Cliques on a Quantum Annealer

2017 ◽  
Author(s):  
Guillaume Chapuis ◽  
Hristo Djidjev ◽  
Georg Hahn ◽  
Guillaume Rizk
Keyword(s):  
Maximum Cliques

Optimal Protein Structure Alignment Using Maximum Cliques

2005 ◽  
Vol 53 (3) ◽  
pp. 389-402 ◽  
Author(s):  
Dawn M. Strickland ◽  
Earl Barnes ◽  
Joel S. Sokol
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Maximum Cliques

Generation of maximum independent sets of a bipartite graph and maximum cliques of a circular-arc graph

1992 ◽  
Vol 13 (1) ◽  
pp. 161-174 ◽  
Author(s):  
Toshinobu Kashiwabara ◽  
Sumio Masuda ◽  
Kazuo Nakajima ◽  
Toshio Fujisawa
Keyword(s):  
Bipartite Graph ◽  
Independent Sets ◽  
Circular Arc ◽  
Maximum Cliques

Finding maximum cliques in circle graphs

Networks ◽  
1981 ◽  
Vol 11 (3) ◽  
pp. 269-278 ◽  
Author(s):  
D. Rotem ◽  
J. Urrutia
Keyword(s):  
Circle Graphs ◽  
Maximum Cliques

A Method to Review Expert Recommendation Using Topic Relevance and Expert Relationship

2018 ◽  
Vol 27 (01) ◽  
pp. 1741004 ◽  
Author(s):  
Shengxiang Gao ◽  
Zhengtao Yu ◽  
Linbin Shi ◽  
Xin Yan ◽  
Haixia Song
Keyword(s):  
New Method ◽  
Markov Network ◽  
Expert Recommendation ◽  
Maximum Cliques ◽  
Topic Relevance ◽  
The Relationship ◽  
Relevant Degree

In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; then, with the information of the two maximum cliques, the relevance between experts and projects is calculated. After that, according to the descending order of the relevant degree, the candidates are ranked. Finally, the experts, who are the top N to projects, are recommended. The experiments on five domain datasets are made and the results show that the proposed method can improve the effect of review expert recommendation, and the F-value increases by an average of 5% than without considering the relevance among topics and the relationship among experts.

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