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Strengthening social networks analysis by networks fusion

Published: 15 January 2020 Publication History

Abstract

The relationship extraction and fusion of networks are the hotspots of current research in social network mining. Most previous work is based on single-source data. However, the relationships portrayed by single-source data are not sufficient to characterize the relationships of the real world. To solve this problem, a Semi-supervised Fusion framework for Multiple Network (SFMN), using gradient boosting decision tree algorithm (GBDT) to fuse the information of multi-source networks into a single network, is proposed in this paper. Our framework aims to take advantage of multi-source networks fusion to enhance the accuracy of the network construction. The experiment shows that our method optimizes the structural and community accuracy of social networks which makes our framework outperforms several state-of-the-art methods.

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Cited By

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  • (2024)Link prediction method for social networks based on a hierarchical and progressive user interaction matrixKnowledge-Based Systems10.1016/j.knosys.2024.111929297(111929)Online publication date: Aug-2024
  • (2023)Ego Network Analysis Using Machine Learning AlgorithmsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-99-4626-6_29(343-352)Online publication date: 11-Oct-2023
  • (2022)Agent-Based Vector-Label Propagation for Explaining Social Network StructuresKnowledge Management in Organisations10.1007/978-3-031-07920-7_24(306-317)Online publication date: 4-Jul-2022
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cover image ACM Conferences
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2019
1228 pages
ISBN:9781450368681
DOI:10.1145/3341161
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

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Author Tags

  1. multi-source networks
  2. networks fusion
  3. semi-supervised learning
  4. social network mining

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  • Short-paper

Funding Sources

  • The National Key Research and Development Program of China
  • Big Data Research Foundation of PICC

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ASONAM '19
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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

View all
  • (2024)Link prediction method for social networks based on a hierarchical and progressive user interaction matrixKnowledge-Based Systems10.1016/j.knosys.2024.111929297(111929)Online publication date: Aug-2024
  • (2023)Ego Network Analysis Using Machine Learning AlgorithmsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-99-4626-6_29(343-352)Online publication date: 11-Oct-2023
  • (2022)Agent-Based Vector-Label Propagation for Explaining Social Network StructuresKnowledge Management in Organisations10.1007/978-3-031-07920-7_24(306-317)Online publication date: 4-Jul-2022
  • (2020)Mining latent academic social relationships by network fusion of multi-type dataSocial Network Analysis and Mining10.1007/s13278-020-00663-610:1Online publication date: 2-Jul-2020

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