A Unified Semi-supervised Framework for Author Disambiguation in Academic Social Network

2014 ◽  
pp. 1-16 ◽  
Author(s):  
Peng Wang ◽  
Jianyu Zhao ◽  
Kai Huang ◽  
Baowen Xu
Keyword(s):  
Social Network ◽  
Academic Social Network ◽  
Author Disambiguation

An Iranian academic social network is coming (Preprint)

2019 ◽  
Author(s):  
Mohammad Dehghani ◽  
Akhondzadeh Shahin ◽  
Mesgarpour Bita ◽  
Ferdousi Reza
Keyword(s):  
Social Network ◽  
Developing Country ◽  
The People ◽  
Academic Social Network ◽  
The Impact

UNSTRUCTURED Iran has faced severe sanctions in recent years from some countries. Due to the dependence of the Iranian health industry on government payments, the health of the people in this country has suffered a lot. One of the solutions for Iran's sanctions to reduce the impact of sanctions on health is to rely on domestic researchers, but researchers in Iran are having problems. One way to reduce researchers' problems is to use the National Academic Social Network. This article describes the steps of setting up an academic social network in a developing country in four stages.

scholarly journals Presenting a dataset for collaborator recommending systems in academic social network

2021 ◽  
Vol 3 (1) ◽  
pp. 29-40
Author(s):  
Zahra Roozbahani ◽  
Jalal Rezaeenour ◽  
Roshan Shahrooei ◽  
Hanif Emamgholizadeh
Keyword(s):  
Social Network ◽  
Academic Social Network

scholarly journals Academic Social Networks: How the web is changing our way to make and communicate researches

2015 ◽  
Vol 7 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Giovanni Bonaiuti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Sites ◽  
Rapid Evolution ◽  
Scientific Field ◽  
Academic Social Network ◽  
Academic Social Networks ◽  
Insight Into ◽  
The Web ◽  
Natural Component

Abstract Networking is not only essential for success in academia, but it should also be seen as a natural component of the scholarly profession. Research is typically not a purely individualistic enterprise. Academic social network sites give researchers the ability to publicise their research outputs and connect with each other. This work aims to investigate the use done by Italian scholars of 11/D2 scientific field. The picture presented shows a realistic insight into the Italian situation, although since the phenomenon is in rapid evolution results are not stable and generalizable.

An analytical system for evaluating academia units based on metrics provided by academic social network

2020 ◽  
Vol 159 ◽  
pp. 113608
Author(s):  
Lukasz Wiechetek ◽  
Kongkiti Phusavat ◽  
Zbigniew Pastuszak
Keyword(s):  
Social Network ◽  
Academic Social Network ◽  
Analytical System

Discovering Academic Communities and their Research Interests

2015 ◽  
Vol 743 ◽  
pp. 607-611
Author(s):  
H.Q. Han ◽  
L.J. Zhu ◽  
Y. Fu ◽  
S. Xu ◽  
Z.F. Zhang ◽  
...  
Keyword(s):  
Social Network ◽  
Resource Management ◽  
Information Resource ◽  
Experimental Results ◽  
Overlapping Communities ◽  
Academic Communities ◽  
Information Resource Management ◽  
Academic Social Network

The paper aims to discover overlapping communities and their research interests in academic social network. The network is constructed based on co-authorships. Cliques are extracted to discover overlapping communities. Keywords used by authors in communities are counted and sorted, and topNkeywords are selected to represent their research interests. The experimental results in the field of Information Resource Management testified the effectiveness of proposed method.

scholarly journals Co-authorship prediction in academic social network

2019 ◽  
Author(s):  
William Takahiro Maruyama ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
The State ◽  
Artificial Intelligence Techniques ◽  
Academic Social Network ◽  
Academic Social Networks

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

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