MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors

Xia, Feng; Chen, Zhe; Wang, Wei; Li, Jing; Yang, Laurence T.

doi:10.1109/tetc.2014.2356505

Cited by 96 publications

(67 citation statements)

References 37 publications

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“…We compare the performance of the HeteroRWR algorithm with several classical baseline methods, including Common Neighbors (CN), Adamic/Adar (AA) [4], the basic RWR and MVCWalker [6]. Since the basic RWR method to be compared is unweighted, there are two versions of the Het-eroRWR algorithm: the unweighted version (HeteroRWR-U) and the weighted version (HeteroRWR).…”

Section: Comparison Methodsmentioning

confidence: 99%

“…RWR is an excellent global similarity-based unsupervised model for link recommendation. Many existing research works use RWR-based models to model user relationship strength [6], [30]- [33]. However, the basic RWR defined in homogeneous networks limits its performance, as mentioned.…”

Section: Related Workmentioning

confidence: 99%

“…Extensive experiments have been performed on DBLP and CiteSeerX datasets. The experimental results show that HeteroRWR outperforms the state-of-the-art baseline methods such as Common Neighbors (CN), Adamic/Adar (AA), Random Walk with Restart (RWR) [4] and MVCWalker [6] models in terms of precicion and recall rate even for the incomplete citation dataset. Nevertheless, the proposed Het-eroRWR approach is time efficient and is thus faciliated to be performed in large-scale networks.…”

Section: Introductionmentioning

confidence: 99%

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HeteroRWR: A Novel Algorithm for Top-<i>k</i> Co-Author Recommendation with Fusion of Citation Networks

Zhao

Peng

Zhang

et al. 2020

IEICE Trans. Inf. & Syst.

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It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable coauthors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that Het-eroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.

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Section: Comparison Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HeteroRWR: A Novel Algorithm for Top-<i>k</i> Co-Author Recommendation with Fusion of Citation Networks

Zhao

Peng

Zhang

et al. 2020

IEICE Trans. Inf. & Syst.

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“…teams) in academic social networks [36], by predicting the formation of links via new and old hyper-edges. Because academic teams tend to evince relationships in different and systematic ways, the hypergraph is a particularly robust datastructure for modeling team formation [40], recommending new collaborations [43] and as a model of how scientists actually seek out new projects [37]. In this paper, instead of assuming a set of edge-types, we show that a distributional measure of link similarity can help leverage the abundance of edge-types found in a text-mined hypergraph of NEs.…”

Section: Background and Related Workmentioning

confidence: 97%

Proposing Ties in a Dense Hypergraph of Academics

Gerow

Lou

Duede

et al. 2015

Lecture Notes in Computer Science

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Abstract. Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships.

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“…For this work the authors do not focus on collaborations leading to co-authorship, though that is well addressed in the literature (see Lee &Bozeman, 2005, andXia, Chen, Wang, Li, &Yang, 2014) and may be an area the authors identify as a possible future area of inquiry with this data set. Abramo et al (2019) describe in their work the advantage of scientific collaboration may have, especially related to attracting different resources and perspectives which result in a wider audience for the research.…”

Section: Limitations Of This Study and Future Researchmentioning

confidence: 99%

An Exploratory Study of Accomplished Librarian-Researchers

Kennedy

Brancolini

Kennedy

2020

EBLIP

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Objective -This work explores potential factors that may contribute to a librarian becoming a highly productive researcher. An understanding of the factors can provide evidence based guidance to those at the beginning of their research careers in designing their own trajectories and to library administrators who seek to create work conditions that contribute to librarian Evidence Based Library and Information Practice 2020, 15.1 180 research productivity. The current study is the first to explore the factors from the perspective of the profession's most accomplished librarian-researchers.Methods -This exploratory and descriptive study recruited 78 academic librarians identified as highly productive researchers; 46 librarians participated in a survey about their professional training and research environments, research networks, and beliefs about the research process. Respondents supplied a recent CV which was coded to produce a research output score for the past 10 years. In addition to fixed-response questions, there were five open-ended questions about possible success factors. All data were analyzed with descriptive statistics and tests of significance correlations.Results -Accomplished librarian-researchers have professional training backgrounds and research environments that vary widely. None is statistically associated with research output. Those with densely connected networks of research colleagues who both know each other and do research together is significantly related to research output. A large group of those identified in the research networks are "both friend and colleague" and offer each other reciprocal support. In open-ended questions, respondents mentioned factors that equally span the three categories of research success: individual attributes, peers and community, and institutional structures.Conclusion -The authors found that that there are many paths to becoming an accomplished librarian-researcher and numerous factors are conducive to achieving this distinction. A positive research environment includes high institutional expectations; a variety of institutional supports for research; and extrinsic rewards, such as salary increases, tenure, promotion, and opportunities for advancement. The authors further conclude that a librarian's research network may be an important factor in becoming an accomplished librarian-researcher. This finding is supported by both the research network analysis and responses to open-ended questions in which collaboration was a frequent theme.

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MVCWalker: Random Walk-Based Most Valuable Collaborators Recommendation Exploiting Academic Factors

Cited by 96 publications

References 37 publications

HeteroRWR: A Novel Algorithm for Top-<i>k</i> Co-Author Recommendation with Fusion of Citation Networks

HeteroRWR: A Novel Algorithm for Top-<i>k</i> Co-Author Recommendation with Fusion of Citation Networks

Proposing Ties in a Dense Hypergraph of Academics

An Exploratory Study of Accomplished Librarian-Researchers

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