ArticlePeerJ. Computer science2019
Dual network embedding for representing research interests in the link prediction problem on co-authorship networks.
Article in PeerJ. Computer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Determining the factors affecting customer satisfaction using an extraction-based feature selection approach.PeerJ. Computer science · 2022Article
- Temporal network embedding framework with causal anonymous walks representations.PeerJ. Computer science · 2022Article
- Survey on graph embeddings and their applications to machine learning problems on graphs.PeerJ. Computer science · 2021Article
- LinkPred: a high performance library for link prediction in complex networks.PeerJ. Computer science · 2021Article
- Fusion of text and graph information for machine learning problems on networks.PeerJ. Computer science · 2021Article
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Authors and funding
4 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.