short-paper

Strengthening social networks analysis by networks fusion

Authors:

Feiyu Long,

Nianwen Ning,

Chenguang Song,

Bin WuAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 460 - 463

https://doi.org/10.1145/3341161.3342939

Published: 15 January 2020 Publication History

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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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Wei SWang LWu HZhou MLi QXiao Y(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
https://doi.org/10.1016/j.knosys.2024.111929
Vaibhav SDhananjay Kumar MHosamani TPatil VNatarajan S(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
https://doi.org/10.1007/978-981-99-4626-6_29
Bellandi VCeravolo PDamiani EMaghool S(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
https://doi.org/10.1007/978-3-031-07920-7_24
Show More Cited By

Strengthening social networks analysis by networks fusion
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval
  2. Information systems applications

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Published In

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

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

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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New York, NY, United States

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Published: 15 January 2020

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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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SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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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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KDD '25

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sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Wei SWang LWu HZhou MLi QXiao Y(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
https://doi.org/10.1016/j.knosys.2024.111929
Vaibhav SDhananjay Kumar MHosamani TPatil VNatarajan S(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
https://doi.org/10.1007/978-981-99-4626-6_29
Bellandi VCeravolo PDamiani EMaghool S(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
https://doi.org/10.1007/978-3-031-07920-7_24
Long FNing NZhang YSong CZhou PWu B(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
https://doi.org/10.1007/s13278-020-00663-6

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Compressing and mining social network data

Interactions around social networks matter: predicting the social network from associated interaction networks

Finding email correspondents in online social networks

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