NDLI: Cross-domain Scientific Collaborations prediction using citation

Content Provider	IEEE Xplore Digital Library
Author	Ying Guo Xi Chen
Copyright Year	2013
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Ying Guo; Xi Chen)
Abstract	Cross-domain Scientific Collaborations have promoted rapid development of science and generated many innovative breakthroughs. However, predicting cross-domain scientific collaboration problem is rarely studied in academic research. Moreover, collaboration recommendation methods within single domain cannot be directly utilized for solving cross-domain problems, because there are topic skewness and sparse connections challenges. In this paper, we propose a Hybrid Graph Model, which combines both explicit co-author relationships and implicit co-citation relationships together to construct graph, then Random Walks with Restarts concept is used to measure and rank relatedness between nodes. Because co-citations appear in both source domain and target domain, they represent the topics which can be shared across domains. In this way, topic skewness problem is solved much cheaper and effectively compared with probabilistic topic models. In addition, co-citation relationship solves sparse connection problem by mining more potential connections between authors. However, few previous works use citation information for scientific collaboration recommendations. Finally, we compare the performances of Hybrid Graph Model with some baseline approaches on large publication data sets from different domains. The experiments show that Hybrid Graph Model outperforms comparison methods on several recommendation metrics. Moreover, citation information has been demonstrated to be very helpful for scientific collaboration recommendations.
Sponsorship	IEEE Comput. Soc.
Starting Page	765
Ending Page	770
File Size	144600
Page Count	6
File Format	PDF
ISBN	9781450322409
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	ACM and IEEE
Subject Keyword	Measurement social network recommender systems Social network services Collaboration link prediction Probabilistic logic Data models Data mining Informatics
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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