Impact of heuristics in clustering large biological networks

• In this paper, we have proposed two new heuristics for clustering biological networks.
• We have incorporated these heuristics into a celebrated clustering algorithm called SPICi to get three new clustering algorithms.
• We have conducted extensive experiments and analysis to analyze the performance of the new algorithm and the results are found to be promising.

Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising.

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