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<p>The use of graph-structured data in applications is increasing day by day. In order to infer
useful information from such data, fast analytics and software tools are required. One of
the graph analytics techniques used is community detection. Community detection is the
technique of finding structural communities within a graph. Such communities are defined
as groups which have highly connected nodes and have similarities with each other.
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<p>This research proposes a parallel heuristic for faster community detection using
the parallel version of the Louvain algorithm: Grappolo. The Louvain algorithm is a
hierarchical algorithm that focuses on modularity optimization. It gained popularity
because of its ability to detect high-quality communities faster than the other existing
community detection algorithms. However, the Louvain algorithm is a sequential
algorithm. To reduce the execution time of the Louvain algorithm, a parallel version
named Grappolo exists in the literature. This algorithm proposes parallel heuristics that
address the challenges that occur due to parallelizing the sequential Louvain algorithm.
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<p>In this study, the researcher is investigating if Grappolo can be further parallelized
to further reduce the execution time maintaining the quality of communities detected. To
evaluate the proposed heuristic, it was tested on an OpenMP multithreaded environment.
It was implemented on source codes of Android malware applications. However, as
compared to Grapplolo, the proposed modified version resulted in higher execution times
for the inputs tested. The modularity of the communities detected was similar to the
Grappolo implementation.
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| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8044526 |
| Date | 15 May 2019 |
| Creators | Pooja Patil (6636122) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/FAST_COMMUNITY_STRUCTURE_ANALYSIS_OF_CALL_GRAPHS_FOR_MALWARE_DETECTION/8044526 |
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