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This algorithm first extracts all maximal cliques from the original complex network.
Clustering by Maximal Cliques (CMC) [ 2] first generates maximal cliques from a network.
For example, a single erroneous interval in only one individual could create two maximal cliques from what should be considered a single recurrent aberration.
Maximal cliques from this graph are extracted which would now form the groups of similar stems that the first step extracts.
Clustering by Maximal Cliques (CMC) [ 3] first generates all the maximal cliques from a given network, and then removes or merges highly overlapping clusters based on their inter-connectivity as follows.
CMC [ 5] gives weight to PPIs using an iterative scoring method to assess the reliability of PPI, finds maximal cliques from the weighted PPI network, and then removes or merges overlapping maximal cliques based on their interconnectivity.
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It is important to point out that if at some point the set not contains a vertex that is adjacent to all vertices in compsub, then the algorithm backtracks since no further selection of candidates will lead to obtaining a maximal clique from the current configuration of the set compsub.
From this graph, co-expressed gene groups were extracted as maximal cliques for each node.
Also a linear time algorithm is designed to find all the maximal cliques of G2 from G. Application of square of interval graphs in the field of L h,k -labelling probL h,k -labellingussed.
50 maximal cliques were derived from this graph, corresponding to the same number of GHCs.
Even the most efficient serial MCE algorithms require large amounts of time to enumerate the maximal cliques in networks arising from these problems that contain hundreds, thousands, or larger numbers of vertices.
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