5 research outputs found
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
ModelDiff: A Framework for Comparing Learning Algorithms
We study the problem of (learning) algorithm comparison, where the goal is to
find differences between models trained with two different learning algorithms.
We begin by formalizing this goal as one of finding distinguishing feature
transformations, i.e., input transformations that change the predictions of
models trained with one learning algorithm but not the other. We then present
ModelDiff, a method that leverages the datamodels framework (Ilyas et al.,
2022) to compare learning algorithms based on how they use their training data.
We demonstrate ModelDiff through three case studies, comparing models trained
with/without data augmentation, with/without pre-training, and with different
SGD hyperparameters. Our code is available at
https://github.com/MadryLab/modeldiff