Social capital and fear of crime in Brisbane

Social capital is upheld for its value in explaining variations in crime across place. Collective efficacy is understood to be the superlative link between less effectual components of neighbourhood social capital (such as social ties and reciprocity) and lower rates of crime. The current study examines the value of neighbourhood social capital in explaining another community attribute associated with neighbourhoods: fear of crime. We conduct a secondary analysis of survey data collected from over 2000 people clustered in 82 Statistical Local Areas in Brisbane, Australia, to examine the correlates of fear of crime. We find that when comparing elements of social capital, the agentic element of social capital – collective efficacy – has the strongest relationship to reduced fear of crime.

The Analysis for the Influence of Individual Node Base on the Network Community

This paper discussed the identification of theoretical research and practical application value that the influence of individual node in the social network node; Secondly, this paper proposes a influence of individual node arithmetic based on network community attribute, and carries on the simulation analysis in the real social network to compared with the influence of individual node that without network community attribute superposition.

Integrating attributes of nodes solves the community structure partition effectively

Revealing ideal community structure efficiently is very important for scientists from many fields. However, it is difficult to infer an ideal community division structure by only analyzing the topology information due to the increment and complication of the social network. Recent research on community detection uncovers that its performance could be improved by incorporating the node attribute information. Along this direction, this paper improves the Blondel–Guillaume–Lambiotte (BGL) method, which is a fast algorithm based on modularity maximization, by integrating the community attribute entropy. To fulfill this goal, our algorithm minimizes the community attribute entropy by removing the boundary nodes which are generated in the modularity maximization at each iteration. By this way, the communities detected by our algorithm make a balance between modularity maximization and community attribute entropy minimization. In addition, another merit of our algorithm is that it is free of parameters. Comprehensive experiments have been conducted on both artificial and real networks to compare the proposed community detection algorithm with several state-of-the-art ones. As the experimental results indicate, our algorithm demonstrates superior performance.