The efficiency of community detection by most similar node pairs

Abstract

Community analysis is an important way to ascertain whether or not a complex system consists of sub-structures with different properties. In this paper, we give a two level community structure analysis for the SSCI journal system by most similar co-citation pattern. Five different strategies for the selection of most similar node (journal) pairs are introduced. The efficiency is checked by the normalized mutual information technique. Statistical properties and comparisons of the community results show that both of the two level detection could give instructional information for the community structure of complex systems. Further comparisons of the five strategies indicates that, the most efficient strategy is to assign nodes with maximum similarity into the same community whether the similarity information is complete or not, while random selection generates small world local community with no inside order. These results give valuable indication for efficient community detection by most similar node pairs.

arXiv commentarXiv admin note: text overlap with arXiv:1706.04829

References in corpus (21)

• Fast unfolding of communities in large networks
• Community structure in social and biological networks
• Modularity and community structure in networks
• Community detection in graphs
• Understanding individual human mobility patterns
• Fast algorithm for detecting community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Hierarchical organization of modularity in metabolic networks
• Functional cartography of complex metabolic networks
• Near linear time algorithm to detect community structures in large-scale networks
• Resolution limit in community detection
• Comparing community structure identification
• Community detection algorithms: a comparative analysis
• Stochastic blockmodels and community structure in networks
• Finding statistically significant communities in networks
• Finding overlapping communities in networks by label propagation
• A Local Method for Detecting Communities
• The many facets of community detection in complex networks
• Currency and commodity metabolites: Their identification and relation to the modularity of metabolic networks
• Surprise maximization reveals the community structure of complex networks
• The way to uncover community structure with core and diversity