Mining potential research synergies from co-authorship graphs using power graph analysis

Authors:
Iraklis Varlamis;George Tsatsaronis
Affiliations:
Department of Informatics and Telematics, Harokopio University of Athens, 89, Harokopou St., Athens 17671, Greece.;Bioinformatics Group, Biotechnology Center (BIOTEC), Technical University of Dresden, Tatzberg 47/49, Dresden 01307, Germany
Venue:
International Journal of Web Engineering and Technology
Year:
2012

Citing 20
Cited 0

Clustering algorithms

Information retrieval
Analysis of papers from twenty-five years of SIGIR conferences: what have we been doing for the last quarter of a century?

ACM SIGIR Forum
Analysis of SIGMOD's co-authorship graph

ACM SIGMOD Record
Multi.Objective Hypergraph Partitioning Algorithms for Cut and Maximum Subdomain Degree Minimization

Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
The author-topic model for authors and documents

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Major Information Visualization Authors, Papers and Topics in the ACM Library

INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Community Mining Tool Using Bibliography Data

IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Graph-based text classification: learn from your neighbors

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Analysis and Visualization of Co-authorship Networks for Understanding Academic Collaboration and Knowledge Domain of Individual Researchers

CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
Visualization of Individual's Knowledge by Analyzing the Citation Networks

CGIV '07 Proceedings of the Computer Graphics, Imaging and Visualisation
DBconnect: mining research community on DBLP data

Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Competence mining for virtual scientific community creation

International Journal of Web Based Communities
BibNetMiner: mining bibliographic information networks

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Introduction to Information Retrieval

Introduction to Information Retrieval
ArnetMiner: extraction and mining of academic social networks

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Semantic relatedness hits bibliographic data

Proceedings of the eleventh international workshop on Web information and data management
Semantic search with GoPubMed

Semantic techniques for the web
Text relatedness based on a word thesaurus

Journal of Artificial Intelligence Research
How to become a group leader? or modeling author types based on graph mining

TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Efficient community detection using power graph analysis

Proceedings of the 9th workshop on Large-scale and distributed informational retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analysed across different dimensions (e.g., author, year, venue, and topic) and can be exploited in multiple ways. The representation and visualisation of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, candidate reviewers for a paper or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualisation tool from the biological domain, we are able to provide comprehensive visualisations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.