Abstract
Due to its importance, influence spread maximization problem for social network has been solved by a number of algorithms. However, when it comes to the scalabilities, existing algorithms are not efficient enough to cope with real-world social networks, which are often big networks. To handle big social networks, we propose parallelized influence spread algorithms. Using Map-Reduce in Hadoop as the platform, we proposed Parallel DAGIS algorithm, a parallel influence spread maximization algorithm. Considering information loss in Parallel DAGIS algorithm, we also develop a Parallel Sampling algorithm and change DFS to BFS during search process. Considering two or even more hops neighbor nodes, we further improve accuracy of DHH. Experimental results show that efficiency has been improved, when coping with big social network, by using Parallel DAGIS algorithm and Parallel Sampling algorithm. The accuracy of DHH has been improved by taking into account more than two hops neighbors.
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Shi, Q., Wang, H., Li, D., Shi, X., Ye, C., Gao, H. (2015). Maximal Influence Spread for Social Network Based on MapReduce. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_16
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DOI: https://doi.org/10.1007/978-3-662-46248-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46247-8
Online ISBN: 978-3-662-46248-5
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