Bipartite structure of all complex networks
Information Processing Letters
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Co-evolution of social and affiliation networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
The purpose of this article is to link high density in social networks with their underlying bipartite affiliation structure. Density is represented by an average number of a node's neighbors (i.e. node degree or node rank). It is calculated by dividing a number of edges in a graph by a number of vertices. We compare an average node degree in real-life affiliation networks to an average node degree in social networks obtained by projecting an affiliation network onto a user modality. We have found recently that the asymptotic Newmann's explicit formula relating node degree distributions in an affiliation network to the density of a projected graph overestimates the latter value for real-life datasets. We have also observed that this property can be attributed to the local tree-like structure assumption. In this article we propose a procedure to estimate the density of a projected graph by means of a mixture of an exponential and a power-law distributions. We show that our method gives better density estimates than the classic formula.