GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

• Main Authors: Sulistianingsih, Neny, Winarko, Edi, Sari, Anny Kartika
• Format: Article PeerReviewed
• Language: English
• Published: Intelligent Network and Systems Society 2022
• Subjects: Information and Computing Sciences; Computer Software; Computer Software not elsewhere classified
• Online Access: https://repository.ugm.ac.id/278758/1/GNPPN-Parallel-GirvanNewmanBased-Algorithm-to-Detect-Communities-in-Graph-with-Positive-and-Negative-WeightsInternational-Journal-of-Intelligent-Engineering-and-Systems.pdf; https://repository.ugm.ac.id/278758/; http://www.inass.org; https://doi.org/10.22266/ijies2022.1231.26
• Institution: Universitas Gadjah Mada

The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN