Title :
Weighted node degree centrality for hypergraphs
Author :
Kapoor, Kalpesh ; Sharma, Divya ; Srivastava, Jaideep
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Minnesota-Twin Cities Minneapolis, Minneapolis, MN, USA
fDate :
April 29 2013-May 1 2013
Abstract :
Many real-world social interactions involve multiple people, for e.g., authors collaborating on a paper, email exchanges made in a company and task-oriented teams in workforce. Simple graph representation of these activities destroys the group structure present in them. Hypergraphs have recently emerged as a better tool for modeling group interactions. However, methods in social hypernetwork analysis haven´t kept pace. In this work, we extend the concept of node degree centrality to hypergraphs. We validate our proposed measures using alternate measures of influence available to us using two datasets namely, the DBLP dataset of scientific collaborations and the group network in a popular Chinese multi-player online game called CR3. We discuss several schemes for assigning weights to hyperedges and compare them empirically. Finally, we define separate weak and strong tie node degree centralities which improves performance of our models. Weak tie degree centrality is found to be a better predictor of influence in hypergraphs than strong tie degree centrality.
Keywords :
computer games; graph theory; social networking (online); CR3; DBLP dataset; graph representation; hypergraphs; popular Chinese multi player online game; real-world social interactions; scientific collaborations; social hypernetwork analysis; weighted node degree centrality; Biological system modeling; Collaboration; Correlation; Electronic mail; Social network services; Time measurement; Weight measurement; Hypergraph; centrality; degree;
Conference_Titel :
Network Science Workshop (NSW), 2013 IEEE 2nd
Conference_Location :
West Point, NY
Print_ISBN :
978-1-4799-0436-5
DOI :
10.1109/NSW.2013.6609212