A Consensus-Based Method to Enhance a Recommendation System for Research Collaboration

Hoang, Dinh Tuyen; Tran, Cuong; Nguyen, Tuong Tri; Nguyên, Ngoc Thanh; Hwang, Dosam

doi:10.1007/978-3-319-54472-4_17

Cited by 8 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recommendation systems have been used in various areas, including e‐commerce, virtual education, and video and music suggestions. Recently, in academic settings, these systems are increasingly used by researchers (Hoang et al, 2017; Liang et al, 2018). Recommendation systems can reduce information overload by providing users with filtered and personalized information.…”

Section: Introductionmentioning

confidence: 99%

“…For example, ResearchGate introduces researchers to each other based on past co‐authoring experiences or studying at the same university. But in these networks, there is still no direct option to introduce a potential collaborator in an integrated and comprehensive manner to take into account all the needs of the user (Hoang et al, 2017). The possibilities of these networks have been investigated in research (Roozbahani et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personalization of the collaborator recommendation system in multi‐layer scientific social networks: A case study of ResearchGate

et al. 2021

View full text Add to dashboard Cite

The development of knowledge sharing platforms, like scientific social networks encourages researchers to establish international collaboration in scientific projects.After reviewing the previous methods for collaborator detection in social networks, gaps in the earlier models are investigated, and the present study aims at filling the gaps by introducing a new scientific collaborator recommendation system. Accordingly, in the present paper, an integrated model is presented based on multilayer networks that can personalize the proposing scientific collaborators. Our proposed model involves various types of collaboration features based on researchers' needs.In our method a scientific social network is modelled as a multi-relational network (MRN), in which collaborators are determined by a community detection algorithm.It provides us with an approach to integrate the personalized features into the collaborator detection model that is due to the essence of semi-supervisory learning of our community detection algorithm. This MRN helps us prevent information loss in the network. We considered two techniques for examining the models. The first one was General Collaborator Recommendation, and the second one was a Collaborator with Personalization Ability. The proposed method is applied to the data of the ResearchGate (RG) social network and is evaluated by criteria, such as F index and NMI.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalization of the collaborator recommendation system in multi‐layer scientific social networks: A case study of ResearchGate

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers have studied 89 million papers published between 1900 and 2015 and found that the collaboration rate between authors has increased 25-fold in the last 116 years. In addition, as measured by the top 1% of cited papers, more than 90% of the world's leading innovations were done by the organization in the early 21st century, which is almost four times as many as in the early 20th century [1]. This suggests that how to find valuable collaborators has become very important [2], and further researchers have demonstrated that scientists or teams with tightly connected collaborative networks tend to generate more research [3].…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the contribution of this paper can be described as follows: (1) We propose a low-order network representation learning model, namely LNLM, to map multiple components into a low-dimensional space while maintaining internal dependencies between components. (2) Comparing to other representa-tion learning model, our proposed model preserves spatially independent structural similarity characteristics in the network.…”

Section: Introductionmentioning

confidence: 99%

Analysis of 5G academic Network based on graph representation learning method

Li¹,

Xu²,

Yu³

et al. 2021

Preprint

View full text Add to dashboard Cite

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and analysis of academic social networks increasingly challenging. Despite the particular success achieved by representation learning in analyzing academic and social networks, most present presentation learning models focus on maintaining the first-order and second-order similarity of nodes. They rarely possess similar structural characteristics of spatial independence in the network. This paper proposes a Low-order Network representation Learning Model (LNLM) based on Non-negative Matrix Factorization (NMF) to solve these problems. The model uses the random walk method to extract low-order features of nodes and map multiple components to a low-dimensional space, effectively maintaining the internal correlation between members. This paper verifies the performance of this model, conducts comparative experiments on four test datasets and four Fully documented templates are available in the elsarticle package on 96 CTAN.

show abstract

“…For instance, different datasets of DBLP have been published so far [3,5], but this data does not include the structural information of the communication network between individuals and only includes the personal information of the researchers. Another dataset provided in the field of collaborator finding is the ResearchGate database [10], which includes only information on user articles.…”

Section: Introductionmentioning

confidence: 99%

Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate

Roozbahani,

Rezaeenour,

Shahrooei

et al. 2021

Preprint

View full text Add to dashboard Cite

Collaborator finding systems are a special type of expert finding models. There is a long-lasting challenge for research in the collaborator recommending research area, which is the lack of a structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset is prepared for designing a consistent, collaborator finding system. The next one, called a co-author finding model, models an academic, social network as a table that contains different relations between the pair of users. Both of them provide an opportunity for introducing potential collaborators to each other. These two models have been extracted from ResearchGate (RG) data set and are available publicly. RG dataset has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users. The dataset consists of almost complete information about users. In the preprocessing phase, the well-known Elmo was used for analyzing textual data. We call this as ResearchGate dataset for Rcommending Systems (RGRS). For assessing the validity of data, we analyze each layer of data separately, and the results are reported. After preparing data and evaluating the collaborator finding models, we have done some assessments on RGRS. Some of these assessments are co-author, following-follower, and question answering relations. The outcomes indicate that it is the best relation in propagating knowledge in the network. To the best of our knowledge, there is no processed and analyzed dataset with this size.

show abstract

A Consensus-Based Method to Enhance a Recommendation System for Research Collaboration

Cited by 8 publications

References 8 publications

Personalization of the collaborator recommendation system in multi‐layer scientific social networks: A case study of ResearchGate

Personalization of the collaborator recommendation system in multi‐layer scientific social networks: A case study of ResearchGate

Analysis of 5G academic Network based on graph representation learning method

Presenting a Dataset for Collaborator Recommending Systems in Academic Social Network: a Case Study on ReseachGate

Contact Info

Product

Resources

About