A Hybrid Probabilistic Graph Based Community Clustering Model for Large Social Networking Link Prediction Data
DOI:
https://doi.org/10.22399/ijcesen.574Keywords:
Social network dataset, Link Prediction, Community Detection, Dynamic clusteringAbstract
Dynamic community clustering is essential for online social networking sites due to the high dimensionality and large data size. It aims to uncover social relationships among nodes and links within the network. However, traditional models often struggle with community structure detection because of the extensive computational time and memory required. Additionally, these models need contextual weighted node information to establish social networking feature relationships. To address these challenges, an advanced probabilistic weighted community detection framework has been developed for large-scale social network data. This framework uses a filter-based probabilistic model to eliminate sparse values and identify weighted community detection nodes for dynamic clustering analysis. Experimental results demonstrate that this filter-based probabilistic community detection framework outperforms others in terms of normalized mutual information, entropy, density, and runtime efficiency (measured in milliseconds).
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