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Community detection is an essential task in network analysis as it helpsidentify groups and patterns within a network. High-speed community detectionalgorithms are necessary to analyze large-scale networks in a reasonable amountof time. Researchers have made significant contributions in the development ofhigh-speed community detection algorithms, particularly in the area oflabel-propagation based disjoint community detection. These algorithms havebeen proven to be highly effective in analyzing large-scale networks in areasonable amount of time. However, it is important to evaluate the performanceand accuracy of these existing methods to determine which algorithm is bestsuited for a particular type of network and specific research problem. In thisreport, we investigate the RAK, COPRA, and SLPA, three label-propagation-basedstatic community discovery techniques. We pay close attention to eachalgorithm's minute details as we implement both its single-threaded andmulti-threaded OpenMP-based variants, making any necessary adjustments oroptimizations and obtaining the right parameter values. The RAK algorithm isfound to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with12 threads was 6.75x faster than the sequential non-strict RAK. The COPRAalgorithm works well with a single label for road networks and max labels of4-16 for other classes of graphs. The SLPA algorithm performs well withincreasing memory size, but overall doesn't offer a favourable return oninvestment. The RAK algorithm is recommended for label-propagation baseddisjoint community detection.

Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection