AIRank: An algorithm on evaluating the academic influence of papers based on heterogeneous academic network

Wang, Mingyang; Zhang, Xinyue; Zhong, Hongwei; Wang, Dailin

doi:10.1177/01655515231151406

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…The calculation of the scholarly node influence of the collaboration subnetwork between scholars is formulated as shown in Eq. ( 19) and (20).…”

Section: B Novelty Indicator Calculation Modulementioning

confidence: 99%

“…3) Influence score: In our previous research [20], an algorithm for evaluating the academic influence of papers based on heterogeneous academic networks, AIRank, was proposed. By distinguishing the differences in the propagation strength of influence among node pairs and comprehensively examining the enhancement effect brought by the influence of heterogeneous neighbors, an effective evaluation of the academic influence of papers is achieved based on heterogeneous academic networks.…”

Section: B Novelty Indicator Calculation Modulementioning

confidence: 99%

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NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

Li,

Wang,

Wang

et al. 2024

IJACSA

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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.

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“…The calculation of the scholarly node influence of the collaboration subnetwork between scholars is formulated as shown in Eq. ( 19) and (20).…”

Section: B Novelty Indicator Calculation Modulementioning

confidence: 99%

Section: B Novelty Indicator Calculation Modulementioning

confidence: 99%