@inproceedings{b248b2d149f94461ac4089b354b1f6b9,
title = "Growing attributed networks through local processes",
abstract = "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×.",
keywords = "Attributed networks, Network Structure, Network growth",
author = "Harshay Shah and Suhansanu Kumar and Hari Sundaram",
note = "Publisher Copyright: {\textcopyright} 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.; 2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",
year = "2019",
month = may,
day = "13",
doi = "10.1145/3308558.3313640",
language = "English (US)",
series = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",
publisher = "Association for Computing Machinery",
pages = "3208--3214",
booktitle = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",
address = "United States",
}