research-article

Attributed Collaboration Network Embedding for Academic Relationship Mining

Authors:
Wei Wang

Dalian University of Technology, China and University of Macau, Taipa, Macau, China

Dalian University of Technology, China and University of Macau, Taipa, Macau, China
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,
Jiaying Liu

Dalian University of Technology, Dalian, China

Dalian University of Technology, Dalian, China
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,
Tao Tang

Dalian University of Technology, Dalian, China

Dalian University of Technology, Dalian, China
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,
Suppawong Tuarob

Mahidol University, Salaya, Nakhon Pathom, Thailand

Mahidol University, Salaya, Nakhon Pathom, Thailand
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,
Feng Xia

Federation University Australia, Australia and Dalian University of Technology, Dalian, China

Federation University Australia, Australia and Dalian University of Technology, Dalian, China

0000-0002-8324-1859
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,
Zhiguo Gong

University of Macau, Taipa, Macau, China

University of Macau, Taipa, Macau, China
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,
Irwin King

The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China

The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China
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ACM Transactions on the Web Volume 15 Issue 1Article No.: 4pp 1–20https://doi.org/10.1145/3409736

Published:24 November 2020Publication History

ACM Transactions on the Web

Abstract

Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding.

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Index Terms

Attributed Collaboration Network Embedding for Academic Relationship Mining
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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Published in
ACM Transactions on the Web Volume 15, Issue 1
February 2021
142 pages
ISSN:1559-1131
EISSN:1559-114X
DOI:10.1145/3432274
Editor:
Brian D. Davison
Tsinghua University, China
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Copyright © 2020 ACM
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Publication History
- Published: 24 November 2020
- Accepted: 1 July 2020
- Revised: 1 June 2020
- Received: 1 November 2019
Published in tweb Volume 15, Issue 1

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Network embedding
academic information retrieval
graph learning
scientific collaboration
Qualifiers
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Attributed Collaboration Network Embedding for Academic Relationship Mining

ACM Transactions on the Web

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Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

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