Modeling bipartite graphs using hierarchical structures

Modeling bipartite graphs using hierarchical structures

Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preser...

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Bibliographic Details
Main Authors: CHUA, Freddy Chong-Tat, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
Bipartite graphs
Bipartite networks
Disassortative structures
Hierarchical random graph model
Network fidelity
Optimal Bipartite hierarchical structure
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/1438
http://doi.org/10.1109/ASONAM.2011.45
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Institution: Singapore Management University
Language: English
Description
Summary:Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preserve the network fidelity as well as the assortative and disassortative structures of bipartite networks. We apply the proposed model on some paper-author networks in DBLP to find their optimal hierarchical structures. Using the optimal bipartite hierarchical structure, we regenerate networks that exhibit the similar network properties and degree distribution as the observed networks.

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