Correlations in bipartite collaboration networks

2005/08/31 by Matti Peltomäki, Matti Peltomaki, Mikko Alava
Business, Management and Accounting · Physics and Astronomy · #Bipartite graph #Business Strategy and Innovation #Complex Network Analysis Techniques #Dynamics (music) #Network structure #Phenomenon #Sublinear function #University-Industry-Government Innovation Models #physics.soc-ph

paper · pdf · doi:10.1088/1742-5468/2006/01/p01010

published as J. Stat. Mech. (2006) P01010 · 13 pages, 17 figures, 2 table, submitted to JSTAT; manuscript reorganized, figures and a table added

arxiv created 2005/11/21 · openalex publication_date 2006/01/20 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05

Abstract

Collaboration networks are studied as an example of growing bipartite networks. These have been previously observed to exhibit structure such as positive correlations between nearest-neighbour degrees. However, a detailed understanding of the origin of such and the growth dynamics is lacking. Both of these issues are analysed empirically and simulated using various models. A new growth model is presented, incorporating empirically necessary ingredients such as bipartiteness and sublinear preferential attachment. This, and a recently proposed model of team assembly, both agree roughly with some empirical observations and fail in several others.

Citations

• Organization of growing random networks
• Dynamical and Correlation Properties of the Internet
• Emergence of scaling in random networks
• Rate equation approach for correlations in growing network models
• Mixing patterns in networks
• Assortative Mixing in Networks
• The simultaneous evolution of author and paper networks
• Statistical mechanics of complex networks
• Evolution of networks
• Evolution of the social network of scientific collaborations
• The Structure and Function of Complex Networks
• Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance
• Uncovering the overlapping community structure of complex networks in nature and society