Abstract
The method of node mining in social networks is divided into heuristic method and greedy method, in which the former mainly measures the importance degree of each node in the networks according to its own attributes or networks topology. As a result, it is fast but with bad accuracy. In the latter algorithm, however, spread simulation has been conducted for each node by adopting diffusion model, and then the importance of each node is calculated through comparison of the spread size. Therefore, this kind of algorithm is inefficient and inappropriate to large-scale social networks. This paper, therefore, proposes a new node mining algorithm based on greedy subgraph, having taken into account the unsatisfactory node mining results in the heuristic method and the highly complexity in greedy algorithm. In this new algorithm, the heuristic method is used first and then the greedy method is also adopted. Then, the author compares the differences of heuristic algorithm, the greedy algorithm and the algorithm proposed in this paper on the effect of node selection, the effectiveness of algorithm, the spread range and so no. it concludes that the node mining algorithm based on greedy subgraph can guarantee good optimal solution on the basis of ensuring a better node selection effect and efficiency than the classical algorithm. Besides, the theoretical applicability and actual spread effect of the proposed algorithm has also been verified.
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Acknowledgments
This work was funded by the National Natural Science Foundation of China under Grant (No. 61772152 and No. 61502037), the Basic Research Project (No. JCKY2016206B001, JCKY2014206C002 and JCKY2017604C010), and the Technical Foundation Project (No. JSQB2017206C002).
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Wang, H., Yin, G., Zhou, L., Zhang, Y., Cao, Z. (2018). Social Networks Node Mining Algorithm of Based on Greedy Subgraph. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11064. Springer, Cham. https://doi.org/10.1007/978-3-030-00009-7_15
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DOI: https://doi.org/10.1007/978-3-030-00009-7_15
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