Community detection and co-author recommendation in co-author networks

Tian, Jin; Wu, Qiong; Ou, Xuan; Yu, Jianjun

doi:10.1007/s13042-020-01190-8

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Although state-of-the-art approaches [23] convert the transition probability of a random walk to dynamically according to each condition of the nodes, it is not enough for recommending authors who have never collaborated because the approach discovers authors based on the probability of the network. To address this issue, Jin et al [24] proposed a method for recommending co-authors by modifying the Hilltop algorithm based on community detection in the co-author network.…”

Section: Related Workmentioning

confidence: 99%

Discovering the role model of authors by embedding research history

Jeon

Jung

2021

Journal of Information Science

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A role model that supports career planning is important for authors in the academic area to improve research abilities. In this study, we discovered a role model in bibliographic networks based on two perspectives: (1) high research performance to be exemplary and (2) a similar research history that can be easily followed by authors. We assume that the year-wise subgraphs in the dynamic bibliographic network signify the ‘research history’. We discovered role models of authors in three steps: (1) learning vector representations of research history in dynamic bibliographic networks, (2) measuring the similarity of authors according to the research history and (3) visualising role models. With this process, we can recommend a reasonable role model whose research path the authors can easily follow. In addition, we verified the effectiveness of the research history embeddings and the accuracy of the recommended role model in a real data set.

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Section: Related Workmentioning

confidence: 99%

Discovering the role model of authors by embedding research history

Jeon

Jung

2021

Journal of Information Science

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“…Link prediction benefits in amplifying the relations in graph-structured data [1], arousing interest from both academia and industries. Existing research mainly focuses on simple graphs where a link (also known as a relation) associates with two entities (also known as an entity), while some real-world relations consist of more than two entities, such as chemical reactions [2], co-authorship relations [3], and social networks [4], etc. As shown in Figure 1 Thus, a hyperlink is coined to model such relations, and the graph comprised of hyperlinks is defined as a hypergraph [5].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Framework for Directed Hypergraph Link Prediction

et al. 2022

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Hypergraphs, as a special type of graph, can be leveraged to better model relationships among multiple entities. In this article, we focus on the task of hyperlink prediction in directed hypergraphs, which finds a wide spectrum of applications in knowledge graphs, chem-informatics, bio-informatics, etc. Existing methods handling the task overlook the order constraints of the hyperlink’s direction and fail to exploit features of all entities covered by a hyperlink. To make up for the deficiency, we present a performant pipelined model, i.e., a two-stage framework for directed hyperlink prediction method (TF-DHP), which equally considers the entity’s contribution to the form of hyperlinks, and emphasizes not only the fixed order between two parts but also the randomness inside each part. The TF-DHP incorporates two tailored modules: a Tucker decomposition-based module for hyperlink prediction, and a BiLSTM-based module for direction inference. Extensive experiments on benchmarks—WikiPeople, JF17K, and ReVerb15K—demonstrate the effectiveness and universality of our TF-DHP model, leading to state-of-the-art performance.

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“…Modern scientific research projects are becoming more and more complex and larger in scale, and their research and development often require multiple researchers to jointly complete [1]. Scientific research cooperation can effectively supplement the scientific research capabilities of scholars, and it is necessary and effective for multiple people to carry out project research and development work in cooperation.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [17] used the method of Louvain [18] to discover the scientific research team in the network and identify the roles of members in the team by constructing a research team citation network. Jin et al [1] detect the community in the co-author network and implement co-author recommendations, which provides the possibility for large-scale research cooperation in the future. Reference [19] builds a network of Italian sociologists, uses the Leiden algorithm [20] to detect co-author communities, and uses the exponential random graph model to find that the cooperative relationship is mainly driven by the research interests of these groups.…”

Section: Introductionmentioning

confidence: 99%

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

Liu

Huang

Zhang

et al. 2021

Scientific Programming

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According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.

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Community detection and co-author recommendation in co-author networks

Cited by 11 publications

References 23 publications

Discovering the role model of authors by embedding research history

Discovering the role model of authors by embedding research history

A Two-Stage Framework for Directed Hypergraph Link Prediction

Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

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