.png){kind=icon}
.png){kind=icon}
Return
An evolutionary non-linear ranking algorithm for ranking scientific collaborations
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-017-0990-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.
Keywords:
Scientific collaboration
Hypergraph
Ranking algorithm
Evolution strategy
Collaboration network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.