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Mining latent academic social relationships by network fusion of multi-type data

Bibliographic Data

ID4658108
AuthorsFeiyu Long (Beijing University of Posts and Telecommunications), Nianwen Ning (0000-0001-9290-0361, Beijing University of Posts and Telecommunications), Yunlei Zhang (0000-0001-9219-6562, North China Institute of Science and Technology, corresponding author), Chenguang Song (0000-0001-9650-880X, Beijing University of Posts and Telecommunications), Pengpeng Zhou (0000-0002-5359-1540, Beijing University of Posts and Telecommunications), Bin Wu (0000-0002-7568-1993, Beijing University of Posts and Telecommunications, corresponding author)
Year2020
Volume10
Issue1
Publication date2020-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-020-00663-6
OpenAlexW3039796744
LanguageEN
Citations received2
References cited28

Data mining · Data science · Machine learning · Social media · Social network analysis · World Wide Web · Advanced Graph Neural Networks · Complex Network Analysis Techniques · Computer Science · Recommender Systems and Techniques · Artificial Intelligence

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  • Entity linking of tweets based on dominant entity candidates

    Open Access•Yue Feng, Fattane Zarrinkalam et al.•Social Network Analysis and Mining•2018

  • Community detection in large-scale social networks

    Open Access•Mehdi Azaouzi, Delel Rhouma et al.•Social Network Analysis and Mining•2019

  • Spark's GraphX-based link prediction for social communication using triangle counting

    Open Access•Ramesh Dharavath, Navaljeet Singh Arora•Social Network Analysis and Mining•2019

  • Enabling cross-site interactions in social networks

    Open Access•Mohamed Shehab, Moonam Ko et al.•Social Network Analysis and Mining•2013

  • Semantically interconnected social networks

    Open Access•Alessandro Cucchiarelli, Fulvio D’antonio et al.•Social Network Analysis and Mining•2012

Unique citing works2
Citations per year0,67
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 2

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