Predicting Scientific Impact via Heterogeneous Academic Network Embedding

Xiao, Chunjing; Han, Jianing; Fan, Wei; Wang, Senzhang; Huang, Rui; Zhang, Yuxiang

doi:10.1007/978-3-030-29911-8_43

Cited by 4 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, an academic network is heterogeneous and is composed of various different kinds of networks, such as co-author network, paper citation network and venuepaper network [2,13]. The graph-based models including univariate and multivariate random walk techniques are widely used to predict future impact of papers [3,[9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

“…Paper citation network with text information (PCNT) [13]. Let P , Z, W respectively represent the sets of papers, their topics and words, E pp , E pz and E pw are the sets of edges between papers and their references, topics, words, E zw is the set of edges between topics and words, respectively.…”

Section: Definitionmentioning

confidence: 99%

“…This can help us predict scientific impact of new papers and new authors. Definition 6 Heterogeneous academic network (HAN) [13]. The HAN is defined as G = (V p ∪ V a , E, F ), where E = {E p ∪ E a ∪ E pa } is the set of different types of edges, and F = {F p ∪ F a ∪ F pa } is the set of weights of edges.…”

Section: Definitionmentioning

confidence: 99%

“…However, these methods (including both univariate or multivariate random-walk algorithms) aim at capturing the global structure information in a simple way to recursively calculate the ranking scores. The local structure information (i.e., local similarity between entities) and the text information of papers (i.e., paper topics), which is helpful to identify influential papers and authors, can not be directly taken into account [13]. Therefore, the prediction accuracy of existing models are relatively poor because of ignoring these two kinds of information.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

Xiao

Sun

Han³

et al. 2021

Appl Intell

Self Cite

View full text Add to dashboard Cite

The prediction of current scientific impact of papers and authors has been extensively studied to help researchers find valuable papers and recent research directions, also help policymakers make recruitment decisions or funding allocation. However, how to accurately evaluate the future impact of them, especially for new papers and young researchers, is the focus of scientific impact prediction research, and is less explored. Existing graph-based methods heavily depend on the global structure information of heterogeneous academic network and ignore the local structure information and text information, which may provide important clues to identify influential papers and authors with novel perspective. In this paper, we propose a hybrid model called ESMR to predict the future influence of papers and authors by mainly exploiting these information mentioned above. Specifically, we first put forward a novel network embedding-based model, which can capture not only the local structure information, but also the text information of papers into a unified embedding representation. Then, the future impact of papers and authors is mutually ranked by integrating the learned embedding representations into a multivariate random-walk model. Empirical results on two real datasets demonstrate that the proposed method significantly outperforms the existing state-of-the-art ranking methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

Xiao

Sun

Han³

et al. 2021

Appl Intell

Self Cite

View full text Add to dashboard Cite

show abstract

“…e existing studies for the research team formation concentrated on synergies between scholars [7][8][9][10]. To predict the synergies, analyzing or embedding bibliographic networks has recently been the most popular approach [11,12]. ese studies searched for adequate collaboration partners of each scholar by analyzing his/her research history.…”

Section: Introductionmentioning

confidence: 99%

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Lee

Hong

Kim

2021

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

Research on Predicting the Impact of Venue Based on Academic Heterogeneous Network

Fang,

Ban

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Predicting Scientific Impact via Heterogeneous Academic Network Embedding

Cited by 4 publications

References 18 publications

Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

Heterogeneous academic network embedding based multivariate random-walk model for predicting scientific impact

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

Research on Predicting the Impact of Venue Based on Academic Heterogeneous Network

Contact Info

Product

Resources

About