# A Greedy Algorithm for Neighborhood Overlap-Based Community Detection
## 3 Neighborhood OVERlap (NOVER)-Based Community Detection Algorithm
### NOVER Algorithm
Though the Louvain algorithm incurs a relatively lower execution time, the NOVER algorithm has been observed to effectively balance the tradeoffs between modularity optimization, execution time and resolution limit (fraction of communities of the smallest size) and incur the largest MREN score for eight of the nine real-world networks analyzed. On the basis of the NMI scores, it appears that the composition of the communities detected by the NOVER algorithm is relatively more closer to that of the GN-efficient and GN-original algorithms, compared to that of the Louvain algorithm. Nevertheless, the composition of the communities detected by the NOVER algorithm appears to be not significantly different from that of the other algorithms (on the basis of the NMI scores). The MREN score proposed in this paper could serve as a global measure (independent of the network size) for evaluating the effectiveness of a community detection algorithm. Both the GN-Efficient and NOVER algorithms adopt a similar design strategy of using an edge measure that is only calculated once (on the input graph itself) and using these initial values of the edge measure to remove the edges (one edge per iteration). The GN-Efficient algorithm removes the edges in the decreasing order of their betweenness values, while the NOVER algorithm removes the edges in the increasing order of their neighborhood overlap values. We observe the NOVER algorithm to provide a higher dividend and detect communities whose cumulative modularity score is relatively larger than that of the communities detected using the GN-efficient algorithm and more closer to the cumulative modularity score of the communities detected using the GN-original algorithm. Thus, the NOVER algorithm is a valuable addition to the literature for community detection and is worth to be investigated for further optimization. As part of future work, we plan to incorporate optimizations (for example: merging of the one-vertex communities) to effectively run the NOVER algorithm for scale-free networks with lower edge-to-node ratio. We plan to compare the performance of the improved NOVER algorithm with that of several edge betweenness-based community detection algorithms (in addition to the Girvan-Newman algorithm). This work is partly funded through the NASA-EPSCoR sub award (# NNX14 AN38A) from University of Mississippi. The author declares no conflict of interest. ## References

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