A Node Importance Measuring Method based on Hypernetwork

Measuring the importance of nodes in network is an important issue in the study of complex networks. The networks in current researches are mostly based on classical graph theory, which have limitations on describing certain complex relations. In this work, we introduce hypernetwork, taking hypergraph as representation foundation, to describe relations. Hypernetwork is able to extend the modeling and describing capability of traditional network and may be a promising representation model of complex relations. However, a general lack of attention to the node importance measuring in hypernetwork, an important fundamental issue for its further application, has been noted across the majority of related published works. In this paper, we utilize the idea of deleting method, to measure the importance of node in hypernetwork through investigating the influence on the whole network when deleting it. Specifically, the influence is measured by direct loss and indirect loss. Through a calculating example, our method is compared with node degree, betweenness, closeness centrality, degree of neighbor nodes etc., the result shows this method has better adaptability and accuracy.

[1]  Leandro Tortosa,et al.  A new betweenness centrality measure based on an algorithm for ranking the nodes of a network , 2014, Appl. Math. Comput..

[2]  Quan Xiao Scientific Research Collaboration Hypernetwork Modeling and Node Importance Measuring , 2014, 2014 International Conference on Management of e-Commerce and e-Government.

[3]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[4]  Masahiro Kimura,et al.  Super mediator - A new centrality measure of node importance for information diffusion over social network , 2016, Inf. Sci..

[5]  J. A. Rodríguez-Velázquez,et al.  Subgraph centrality in complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Jun Hu,et al.  Evaluating Node Importance with Multi-Criteria , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[7]  Yingmin Wu,et al.  Hypergraph Model of Prior Knowledge in Opportunity Discovery , 2008, 2008 International Symposium on Knowledge Acquisition and Modeling.

[8]  Li Peng-xiang,et al.  An Importance Measure of Actors (Set) within a Network , 2004 .

[9]  Mark E. J. Newman A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[10]  Zhao-Long Hu,et al.  A Knowledge Generation Model via the Hypernetwork , 2014, PloS one.

[11]  Y. She Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods , 1985 .

[12]  Daniel Schall Measuring contextual partner importance in scientific collaboration networks , 2013, J. Informetrics.

[13]  K. Goh,et al.  Betweenness centrality correlation in social networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Anna Nagurney,et al.  The Evolution and Emergence of Integrated Social and Financial Networks with Electronic Transactions: A Dynamic Supernetwork Theory for the Modeling, Analysis, and Computation of Financial Flows and Relationship Levels , 2004 .

[15]  Rong Luo,et al.  A novel centrality method for weighted networks based on the Kirchhoff polynomial , 2015, Pattern Recognit. Lett..

[16]  Nicole Coviello,et al.  Entrepreneurship Research on Network Processes: A Review and Ways Forward , 2010 .

[17]  Andrea Landherr,et al.  A Critical Review of Centrality Measures in Social Networks , 2010, Bus. Inf. Syst. Eng..

[18]  U. Brandes A faster algorithm for betweenness centrality , 2001 .