Abstract
Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.
Highlights
Ongoing advances in information technology (IT), and the exponential growth of social networks, are main drivers for the growing global connections of businesses and individuals [1].Identifying key network nodes is an important problem in network information theory [2] that helps in the analysis of complex multi-agent systems such as social networks
1, would be more central according to closeness centrality
A node with high degree would be the most central according to degree centrality
Summary
Ongoing advances in information technology (IT), and the exponential growth of social networks, are main drivers for the growing global connections of businesses and individuals [1]. Identifying key network nodes (or influencing nodes) is an important problem in network information theory [2] that helps in the analysis of complex multi-agent systems such as social networks. The main feature of social networks is that their structure develops via mutual connections between network members. Social network analysis (SNA) helps in the mapping relationships between network entities and identifying the patterns of behavior in a network [3], in understanding the dynamic evolution or relationships within the user community over time, which may provide a solution for non-standard analytical problems. Regularities, or patterns in relationships between social entities, can be used to characterize the social environment and even predict its further evolution [4], especially for rapidly evolving social commerce networks (e.g., Alibaba, Sina Weibo) and traditional ones [5]
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