2 research outputs found
Relay-Linking Models for Prominence and Obsolescence in Evolving Networks
The rate at which nodes in evolving social networks acquire links (friends,
citations) shows complex temporal dynamics. Preferential attachment and link
copying models, while enabling elegant analysis, only capture rich-gets-richer
effects, not aging and decline. Recent aging models are complex and heavily
parameterized; most involve estimating 1-3 parameters per node. These
parameters are intrinsic: they explain decline in terms of events in the past
of the same node, and do not explain, using the network, where the linking
attention might go instead. We argue that traditional characterization of
linking dynamics are insufficient to judge the faithfulness of models. We
propose a new temporal sketch of an evolving graph, and introduce several new
characterizations of a network's temporal dynamics. Then we propose a new
family of frugal aging models with no per-node parameters and only two global
parameters. Our model is based on a surprising inversion or undoing of triangle
completion, where an old node relays a citation to a younger follower in its
immediate vicinity. Despite very few parameters, the new family of models shows
remarkably better fit with real data. Before concluding, we analyze temporal
signatures for various research communities yielding further insights into
their comparative dynamics. To facilitate reproducible research, we shall soon
make all the codes and the processed dataset available in the public domain
Growing Attributed Networks through Local Processes
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 rich structural properties found in real-world
networks. We make three contributions. First, we propose a parsimonious and
accurate model of attributed network growth that jointly explains the emergence
of in-degree distributions, local clustering, clustering-degree relationship
and attribute mixing patterns. Second, our model is based on biased random
walks and uses local processes to form edges without recourse to global network
information. Third, we account for multiple sociological phenomena: bounded
rationality, structural constraints, triadic closure, attribute homophily, and
preferential attachment. Our experiments indicate that the proposed Attributed
Random Walk (ARW) model accurately preserves network structure and attribute
mixing patterns of six real-world networks; it improves upon the performance of
eight state-of-the-art models by a statistically significant margin of 2.5-10x.Comment: 11 pages, 13 figure