TY - GEN
T1 - Growing attributed networks through local processes
AU - Shah, Harshay
AU - Kumar, Suhansanu
AU - Sundaram, Hari
N1 - Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of structural properties found in real-world networks. We make three contributions. First, we propose a simple and accurate model of attributed network growth that jointly explains the emergence of in-degree, local clustering, clustering-degree relationship and attribute mixing patterns. Second, we make use of biased random walks to develop a model that forms edges locally, without recourse to global information. Third, we account for multiple sociological phenomena: bounded rationality; structural constraints; triadic closure; attribute homophily; preferential attachment. Our experiments show that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of real-world networks; it improves upon the performance of eight well-known models by a significant margin of 2.5-10×.
AB - This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of structural properties found in real-world networks. We make three contributions. First, we propose a simple and accurate model of attributed network growth that jointly explains the emergence of in-degree, local clustering, clustering-degree relationship and attribute mixing patterns. Second, we make use of biased random walks to develop a model that forms edges locally, without recourse to global information. Third, we account for multiple sociological phenomena: bounded rationality; structural constraints; triadic closure; attribute homophily; preferential attachment. Our experiments show that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of real-world networks; it improves upon the performance of eight well-known models by a significant margin of 2.5-10×.
KW - Attributed networks
KW - Network Structure
KW - Network growth
UR - http://www.scopus.com/inward/record.url?scp=85066904786&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066904786&partnerID=8YFLogxK
U2 - 10.1145/3308558.3313640
DO - 10.1145/3308558.3313640
M3 - Conference contribution
AN - SCOPUS:85066904786
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 3208
EP - 3214
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
T2 - 2019 World Wide Web Conference, WWW 2019
Y2 - 13 May 2019 through 17 May 2019
ER -