TITLE:
The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms
AUTHORS:
Peng Zuo, Zhen Jia
KEYWORDS:
Bipartite Networks, Evolving Model, Semi-Bipartite Networks, Hybrid Attachment, Degree Distribution
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.10,
October
25,
2023
ABSTRACT: The bipartite graph structure exists in the connections of many objects
in the real world, and the evolving modeling is a good method to describe and
understand the generation and evolution within various real complex networks.
Previous bipartite models were proposed to mostly explain the principle of
attachments, and ignored the diverse growth speed of nodes of sets in different
bipartite networks. In this paper, we propose an evolving bipartite network
model with adjustable node scale and hybrid attachment mechanisms, which uses
different probability parameters to control the scale of two disjoint sets of
nodes and the preference strength of hybrid attachment respectively. The
results show that the degree distribution of single set in the proposed model
follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is
equal to 0. Furthermore, we extend the previous model to a semi-bipartite
network model, which embeds more
user association information into the internal network, so that the model is
capable of carrying and revealing more deep information of each user in the
network. The simulation results of two models are in good agreement with the
empirical data, which verifies that the models have a good performance on real
networks from the perspective of degree distribution. We believe these two
models are valuable for an explanation of the origin and growth of bipartite
systems that truly exist.