Collaborator recommendation in heterogeneous bibliographic networks using random walks

Xing, Zhou; Ding, Lixin; Li, Zhaokui; Wan, Runze

doi:10.1007/s10791-017-9300-3

Cited by 36 publications

(13 citation statements)

References 34 publications

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“…ere exist some works that stand on the shoulder of random walk for academic collaborator recommendation, which had been proved to be competent for calculating the rank score of researchers in the academic collaborator network [33]. ese methods completely utilized the weight on edge to guide Random Walker on the academic collaborator network [24,34,35]. ese values of weight were composed of the affiliated institution of the researcher or the academic factors, such as coauthor order, latest collaboration time, and the times of collaboration.…”

Section: Related Workmentioning

confidence: 99%

Content‐Enhanced Network Embedding for Academic Collaborator Recommendation

et al. 2021

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It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content-enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content-enhanced academic collaborator network based on the weighted text representation of each researcher. The content-enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content-enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top- k similar researchers are recommended for each target researcher. Experiment results on the real-world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.

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Section: Related Workmentioning

confidence: 99%

Content‐Enhanced Network Embedding for Academic Collaborator Recommendation

et al. 2021

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“…erefore, various studies [8-10, 12, 20-24] attempted to extract structural features of the bibliographic networks and to apply to predicting future coauthorship. To deal with the structural features, affinity propagation based on random walks was the most popular [9,10,20,25]. However, recently, network embedding models enable us to represent the structural features by using low-dimensional fixed-length vectors [1,5,6,12,26].…”

Section: Related Workmentioning

confidence: 99%

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Lee

Hong

Kim

2021

Mobile Information Systems

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This study aims at forming research teams for interinstitutional collaborations. Research institutes have their own purposes and topics of interest. Thus, supporting joint research between multiple institutes, we have to consider not only synergies between scholars but also purposes of the institutes. To solve this problem, we propose a bibliographic network embedding method that can learn characteristics of institutes, not only of each scholar. First, we compose a bibliographic network that consists of scholars, publications, venues, research projects, and institutes. Collaboration styles and research topics of institutes and scholars are extracted by mining subgraphs from the bibliographic network. Then, vector representations of network nodes are learned based on occurrences of subgraphs on the nodes and neighborhoods of the nodes. Based on the vector representations, we train multilayer perceptrons (MLP) to assess collaboration probability between scholars affiliated in different institutes. For training the MLP, we suggest three strategies: (i) considering every collaboration, (ii) focusing on interinstitutional collaborations, and (iii) focusing on collaboration outcomes. To evaluate the proposed methods, we have analyzed research collaborations of POSTECH (Pohang University of Science and Technology) and RIST (Research Institute of Industrial Science and Technology) from 2011 to 2020. Then, we conducted the research team formation for joint research of the two institutes according to two purposes: pure research and commercialization research.

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“…In a heterogeneous network, Zhou Ding, Li, and Wan (2017) introduces an algorithm named Random Walk with Restart-based Collaborator Recommendation (RWR-CR) to solve the collaborator recommendation problem. This method includes three steps: 1) heterogeneous bibliographic network construction; 2) edge weighting; and 3) random walk with the restart.…”

Section: Related Workmentioning

confidence: 99%

Smart Collaborative Learning

Akhrif

Benfares

Idrissi

et al. 2019

International Journal of Smart Security Technologies

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Technologically enhanced learning has shifted from digital resources to smart components to afford more content, support tools, and provide learning guidance that meets a learner's needs and interests by delivering smart university services. Smart interaction is essential in smart university, it is a concept that offers new opportunities and new channels of communication between learners. This communication is reinforced by the concept of collaboration, an important factor for knowledge sharing. The current study concerns team building based on the recommendation of the most appropriate collaborator in order to make groups of learners promoting universal participation of all members of the team. The complexity of this problem requires collaborative filtering algorithms to find the potential collaborators for each learner, taking into account problem-solving as a parameter representing items of the recommendation matrix.

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Collaborator recommendation in heterogeneous bibliographic networks using random walks

Cited by 36 publications

References 34 publications

Content‐Enhanced Network Embedding for Academic Collaborator Recommendation

Content‐Enhanced Network Embedding for Academic Collaborator Recommendation

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Smart Collaborative Learning

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