Blog
About

21
views
0
recommends
+1 Recommend
1 collections
    1
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Dual network embedding for representing research interests in the link prediction problem on co-authorship networks

      1 , 2 , 1 , 1 , 1

      PeerJ Computer Science

      PeerJ

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We present a study on co-authorship network representation based on network embedding together with additional information on topic modeling of research papers and new edge embedding operator. We use the link prediction (LP) model for constructing a recommender system for searching collaborators with similar research interests. Extracting topics for each paper, we construct keywords co-occurrence network and use its embedding for further generalizing author attributes. Standard graph feature engineering and network embedding methods were combined for constructing co-author recommender system formulated as LP problem and prediction of future graph structure. We evaluate our survey on the dataset containing temporal information on National Research University Higher School of Economics over 25 years of research articles indexed in Russian Science Citation Index and Scopus. Our model of network representation shows better performance for stated binary classification tasks on several co-authorship networks.

          Related collections

          Most cited references 16

          • Record: found
          • Abstract: found
          • Article: not found

          Coauthorship networks and patterns of scientific collaboration.

           Aaron Newman (2004)
          By using data from three bibliographic databases in biology, physics, and mathematics, respectively, networks are constructed in which the nodes are scientists, and two scientists are connected if they have coauthored a paper. We use these networks to answer a broad variety of questions about collaboration patterns, such as the numbers of papers authors write, how many people they write them with, what the typical distance between scientists is through the network, and how patterns of collaboration vary between subjects and over time. We also summarize a number of recent results by other authors on coauthorship patterns.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Link Prediction in Complex Networks: A Survey

               Linyuan Lü,  Tao Zhou (2010)
              Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.
                Bookmark

                Author and article information

                Journal
                PeerJ Computer Science
                PeerJ
                2376-5992
                2019
                January 21 2019
                : 5
                : e172
                Affiliations
                [1 ]School of Data Analysis and Artificial Intelligence, National Research University Higher School of Economics, Moscow, Russia
                [2 ]Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
                Article
                10.7717/peerj-cs.172
                © 2019

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                Product
                Self URI (journal-page): https://peerj.com/computer-science/
                Self URI (article page): https://peerj.com/articles/cs-172

                Computer science

                Comments

                Comment on this article