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Consensus-based aggregation for identification and ranking of top-k influential nodes

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Abstract

Technology has continuously been a crucially influenced and acutely tangled with the progress of society. Online Social Networks (OSN) are interesting and valuable datasets that can be leveraged to improve understanding about society and to know inter-personal choices. Identification and Ranking of Influential Nodes (IRIN) is non-trivial task for real time OSN like Twitter which accustom with ever-changing network, demographics and contents having heterogeneous features such as Tweets, Likes, Mentions and Retweets. Existing techniques such as Centrality Measures and Influence Maximization ignores vital information available on OSN, which are inappropriate for IRIN. Most of these approaches have high computational complexity i.e. \(O(n^{3} )\). This research aims to put forward holistic approach using Heterogeneous Surface Learning Features (HSLF) for IRIN on specific topic and proposes two approaches: Average Consensus Ranking Aggregation and Weighted Average Consensus Ranking Aggregation using HSLF. The effectiveness and efficiency of the proposed approaches are tested and analysed using real world data fetched from Twitter for two topics, Politics and Economy and achieved superior results compared to existing approaches. The empirical analysis validate that the proposed approach is highly scalable with low computational complexity and applicable for large datasets.

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Authors and Affiliations

  1. Department of Computer Engineering, SVNIT, Surat, India

    Bharat Tidke, Rupa Mehta & Jenish Dhanani

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  1. Bharat Tidke
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  2. Rupa Mehta
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  3. Jenish Dhanani
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Correspondence to Bharat Tidke.

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Appendix

Appendix

See Tables 8 and 9.

Table 8 Identified and ranked influential nodes for various approaches for politics data
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Table 9 Identified and ranked influential nodes for various approaches for economy data
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Tidke, B., Mehta, R. & Dhanani, J. Consensus-based aggregation for identification and ranking of top-k influential nodes. Neural Comput & Applic 32, 10275–10301 (2020). https://doi.org/10.1007/s00521-019-04568-0

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