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An approximate framework for scaling social influence computation in large networks

Published: 24 March 2014 Publication History

Abstract

Social networking service platforms have gained a great success in recent years. Analyzing the social network data from the platforms presents new opportunities for various applications. Among the applications, the social influence analysis has gained great attentions, which provide great business values in helping companies determine which potential customers to market to. However, as social networks become increasingly large, scalability is quickly becoming the major challenge for conducting the social influence analysis in large-scale social networks. To this point, the common practice is to adopt parallel processing model. However, from the initial experimentation, we find that the traffics load between nodes is very high, and becomes a bottleneck for analysis. In this paper, we present a novel approximation framework which significantly reduces the amount of data traffics for processing social influence analysis. The proposed framework exhibit high efficiency and ensures a tunable (ε, δ) accuracy constraint, which guarantees the error in the reported result is within a factor of ε with probability (1--δ). In addition, we conduct a comprehensive performance evaluation to validate and evaluate the proposed techniques. The experimental results clearly show the superiority of the proposed framework.

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  • (2017)Enabling in-network aggregation by diffusion units for urban scale M2M networksJournal of Network and Computer Applications10.1016/j.jnca.2017.05.00293(215-227)Online publication date: Sep-2017

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  1. An approximate framework for scaling social influence computation in large networks

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 March 2014

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    Author Tags

    1. approximate algorithm
    2. business intelligence
    3. parallel processing
    4. social influence analysis
    5. social networking

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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    • (2017)Enabling in-network aggregation by diffusion units for urban scale M2M networksJournal of Network and Computer Applications10.1016/j.jnca.2017.05.00293(215-227)Online publication date: Sep-2017

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