Degree distributions of bipartite networks and their projections
arXiv:1802.04953 · doi:10.1103/PhysRevE.98.022307
Abstract
Bipartite (two-mode) networks are important in the analysis of social and economic systems as they explicitly show conceptual links between different types of entities. However, applications of such networks often work with a projected (one-mode) version of the original bipartite network. The topology of the projected network, and the dynamics that take place on it, are highly dependent on the degree distributions of the two different node types from the original bipartite structure. To date, the interaction between the degree distributions of bipartite networks and their one-mode projections is well understood for only a few cases, or for networks that satisfy a restrictive set of assumptions. Here we show a broader analysis in order to fill the gap left by previous studies. We use the formalism of generating functions to prove that the degree distributions of both node types in the original bipartite network affect the degree distribution in the projected version. To support our analysis, we simulate several types of synthetic bipartite networks using a configuration model where node degrees are assigned from specific probability distributions, ranging from peaked to heavy-tailed distributions. Our findings show that when projecting a bipartite network onto a particular set of nodes, the degree distribution for the resulting one-mode network follows the distribution of the nodes being projected on to, but only so long as the degree distribution for the opposite set of nodes does not have a heavier tail. Furthermore, we show that bipartite degree distributions are not the only feature driving topology formation of projected networks, in contrast to what is commonly described in the literature.
arXiv comment14 pages, 5 figures, 3 tables
References in corpus (24)
- The structure and function of complex networks
- Lethality and centrality in protein networks
- Assortative mixing in networks
- Finding community structure in networks using the eigenvectors of matrices
- The structure of scientific collaboration networks
- The diameter of the world wide web
- The architecture of complex weighted networks
- Random graphs with arbitrary degree distributions and their applications
- The Building Blocks of Economic Complexity
- Mixing patterns in networks
- Classes of behavior of small-world networks
- Who is the best connected scientist? A study of scientific coauthorship networks
- Why social networks are different from other types of networks
- How to project a bipartite network?
- Modularity and community detection in bipartite networks
- Bipartite Graphs as Models of Complex Networks
- Self-organization of collaboration networks
- The clustering coefficient and community structure of bipartite networks
- Efficiently inferring community structure in bipartite networks
- Complex network study of Brazilian soccer players
- Emergence of a non-scaling degree distribution in bipartite networks: a numerical and analytical study
- Weighted Trade Network in a Model of Preferential Bipartite Transactions
- Modeling Discrete Combinatorial Systems as Alphabetic Bipartite Networks: Theory and Applications
- Understanding how both the partitions of a bipartite network affect its one-mode projection
Cited by in corpus (8)
- Transitivity and degree assortativity explained: The bipartite structure of social networks
- Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships
- Meta-validation of bipartite network projections
- Modularity and Projection of Bipartite Networks
- Dynamical Complex Network Models of the Turbulent Cascade
- The role of bipartite structure in R&D collaboration networks
- Structure dynamics of evolving scientific networks
- Scale-Invariant Strength Assortativity of Streaming Butterflies