Intelligent Community Detection: Comparative Study (COVID19 Dataset)

Moosa, J.; Awad, W.; Kalganova, T.

Community detection is an important tool for analyzing networks;it can help us understand the structures and functional characteristics. Network communities represent a principled way of organizing real-world networks into densely connected groups of nodes, whereas a community is a cluster of nodes that are strongly connected to each other in a subnetwork than to the rest of the network. This has remarkable results in various fields, e.g., social science, bibliometrics, marketing and recommendations, biology etc. This research proposes a dataset based on COVID19 distribution. The network dataset is formed by tracing the transmission of the virus among the world countries. This experiment demonstrates the spread of COVID19 and its mutated strain. Several algorithms such as Girvan Newman, Greedy, Louvain, Clustering, and Label Propagation are implemented on the proposed dataset, and the results are evaluated using the modularity measure. The proposed COVID 19 dataset demonstrated the properties and dynamic behavior similar to Zachry’s Karate dataset. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.