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This paper proposes an attributed network growth model. Despite the knowledgethat individuals use limited resources to form connections to similar others,we lack an understanding of how local and resource-constrained mechanismsexplain the emergence of rich structural properties found in real-worldnetworks. We make three contributions. First, we propose a parsimonious andaccurate model of attributed network growth that jointly explains the emergenceof in-degree distributions, local clustering, clustering-degree relationshipand attribute mixing patterns. Second, our model is based on biased randomwalks and uses local processes to form edges without recourse to global networkinformation. Third, we account for multiple sociological phenomena: boundedrationality, structural constraints, triadic closure, attribute homophily, andpreferential attachment. Our experiments indicate that the proposed AttributedRandom Walk (ARW) model accurately preserves network structure and attributemixing patterns of six real-world networks; it improves upon the performance ofeight state-of-the-art models by a statistically significant margin of 2.5-10x.

Growing Attributed Networks through Local Processes