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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

Incorporating Assortativity and Degree Dependence into Scalable Network Models

March 8, 2023

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Authors

Stephen Mussmann

Purdue University


John Moore

Purdue University


Joseph Pfeiffer

Purdue University


Jennifer Neville

Purdue University


DOI:

10.1609/aaai.v29i1.9207


Abstract:

Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable generative graph models focus on modeling distributions of graphs that match real world network properties and scale to large datasets. Much work has focused on modeling networks with a power law degree distribution, clustering, and small diameter. In network analysis, the assortativity statistic is defined as the correlation between the degrees of linked nodes in the network. The assortativity measure can distinguish between types of networks---social networks commonly exhibit positive assortativity, in contrast to biological or technological networks that are typically disassortative. Despite this, little work has focused on scalable graph models that capture assortativity in networks. The contributions of our work are twofold. First, we prove that an unbounded number of pairs of networks exist with the same degree distribution and assortativity, yet different joint degree distributions. Thus, assortativity as a network measure cannot distinguish between graphs with complex (non-linear) dependence in their joint degree distributions. Motivated by this finding, we introduce a generative graph model that explicitly estimates and models the joint degree distribution. Our Binned Chung Lu method accurately captures both the joint degree distribution and assortativity, while still matching characteristics such as the degree distribution and clustering coefficients. Further, our method has subquadratic learning and sampling methods that enable scaling to large, real world networks. We evaluate performance compared to other scalable graph models on six real world networks, including a citation network with over 14 million edges.

Topics: AAAI

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HOW TO CITE:

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville Incorporating Assortativity and Degree Dependence into Scalable Network Models Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville Incorporating Assortativity and Degree Dependence into Scalable Network Models AAAI 2015, .

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville (2015). Incorporating Assortativity and Degree Dependence into Scalable Network Models. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. Incorporating Assortativity and Degree Dependence into Scalable Network Models. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. 2015. Incorporating Assortativity and Degree Dependence into Scalable Network Models. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. (2015) "Incorporating Assortativity and Degree Dependence into Scalable Network Models", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville, "Incorporating Assortativity and Degree Dependence into Scalable Network Models", AAAI, p., 2015.

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. "Incorporating Assortativity and Degree Dependence into Scalable Network Models". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. "Incorporating Assortativity and Degree Dependence into Scalable Network Models". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

Stephen Mussmann|| John Moore|| Joseph Pfeiffer|| Jennifer Neville. Incorporating Assortativity and Degree Dependence into Scalable Network Models. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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