HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding

Liu, Xiaoyu; Wu, Kun; Liu, Biao; Qiu, Rong

doi:10.1016/j.ipm.2022.103253

Cited by 24 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although bipartite and tripartite graphs can integrate heterogeneous information, they cannot integrate more types of objects and relationships. Liu et al [15] modeled data heterogeneity based on a heterogeneous information network to provide scientific collaborator recommendations. Li et al [33] proposed an approach based on a heterogeneous information network for making paper recommendations.…”

Section: Network-based Recommendation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

et al. 2023

View full text Add to dashboard Cite

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

show abstract

Section: Network-based Recommendation Methodsmentioning

confidence: 99%

“…Xu et al [14] proposed a heterogeneous information network model to provide scholar-friend recommendations. Liu et al [15] proposed a method based on heterogeneous information network embedding for scientific collaborator recommendation. Unfortunately, the performance of these methods is limited by three factors.…”

Section: Introductionmentioning

confidence: 99%

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Jagadishwari et al [9] used a collaborative filtering method to help identify collaborators based on the research interests and the papers published by the researchers. Liu et al [10] proposed a heterogeneous network embedding recommendation model HNERec. This method uses four metapath random walks of topic relationship, authorship, citation relationship, and venue relationship to traverse the heterogeneous network randomly, and utilizes the skip-gram model to embed the nodes, and finally generates a recommendation list based on the similarity between the corresponding node vectors.…”

Section: Related Workmentioning

confidence: 99%

NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

Li,

Wang,

Wang

et al. 2024

IJACSA

View full text Add to dashboard Cite

Collaborator recommendation is a crucial topic in research management. This paper proposes a Novelty-Oriented Scientific Research Collaborator recommendation model (NovSRC). By recommending collaborators under the guidance of novel indicators, NovSRC aims to broaden scholars' research perspectives and facilitate the progress of research innovation. NovSRC utilizes heterogeneous academic networks composed of different academic entities and their relationships to learn vector representations of scholars and quantify their novelty metrics. A weighted academic collaboration network was constructed by measuring the novelty collaboration strength (NCS) among scholars under the novelty index, and based on this network, the final vector representation of scholars under the guidance of novelty characteristics was learned. By calculating the similarity between scholar vectors, NovSRC generates a Top-N recommendation list with a focus on novelty. The experimental results indicate that NovSRC achieved the best recommendation performance. Compared with the baseline models, the recommendation precision of NovSRC has improved by 6.9%, the F1 value has increased by 17.3%, and the novelty collaboration strength among scholars has increased by 3.3%. The analysis of the recommended list shows that compared to the target scholars, scholars recommended by the NovSRC model exhibit a wider distribution of research interests, which confirms that novelty has become a key benchmark factor for scholars seeking collaborators.

show abstract

“…This work uses the dataset from Aminer 1. As the release dataset version continues to update, it has become more popular and used for analyzing the information spread [33], studying the scientific influence [34][35][36], building recommendations in academic networks [37,38], researching citation and cooperation networks [39][40][41][42], and developing the prediction in academic networks [43,44]. This work adopts the 12th version of the dataset, which includes 4.9 million papers from 113,887 disciplines.…”

Section: Data Preparationmentioning

confidence: 99%

The profit and risk in the interdisciplinary behavior

Luo

Liang

et al. 2023

Front. Phys.

View full text Add to dashboard Cite

Evaluating the influence of interdisciplinary research is important to the development of science. This work considers the large and small disciplines, calculates the interdisciplinary distance, and analyzes the influence of interdisciplinary behavior and interdisciplinary distance in the academic network. The results show that the risk of interdisciplinary behavior in the large discipline is more significant than the benefits. The peer in the small disciplines will tend to agree with the results of the small discipline across the large discipline. We further confirmed this conclusion by utilizing PSM-DID. The analysis between interdisciplinary distance and scientists’ influence shows that certain risks will accompany any distance between disciplines. However, there still exists a “Sweet Spot” which could bring significant rewards. Overall, this work provides a feasible approach to studying and understanding interdisciplinary behaviors in science.

show abstract

HNERec: Scientific collaborator recommendation model based on heterogeneous network embedding

Cited by 24 publications

References 26 publications

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

The profit and risk in the interdisciplinary behavior

Contact Info

Product

Resources

About