Networked Data Mining Based on Social Network Visualizations

Yang, Yu-Bin; Zhang, Yao

doi:10.3724/sp.j.1001.2008.01980

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Social networking has become so popular such that users have drifted from the traditional physical media (the print and broadcasting media) to a more sophisticated social interaction. Computer scientists are looking into designing tools, models, and applications in diverse fields in internet application technologies [1] such as communication [2], recommendations, social marketing, terrorist threats [3], and key node mining [4]. Efficiently computing the shortest path between any two nodes in a network is one of the most important concerns of researchers since it has a great potential for mobilizing people [5].…”

Section: Introductionmentioning

confidence: 99%

Approximation Algorithm for Shortest Path in Large Social Networks

2020

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Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall’s algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.

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