13,287 research outputs found
Stochastic network formation and homophily
This is a chapter of the forthcoming Oxford Handbook on the Economics of
Networks
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Logistics of Mathematical Modeling-Focused Projects
This article addresses the logistics of implementing projects in an
undergraduate mathematics class and is intended both for new instructors and
for instructors who have had negative experiences implementing projects in the
past. Project implementation is given for both lower and upper division
mathematics courses with an emphasis on mathematical modeling and data
collection. Projects provide tangible connections to course content which can
motivate students to learn at a deeper level. Logistical pitfalls and insights
are highlighted as well as descriptions of several key implementation
resources. Effective assessment tools, which allowed me to smoothly adjust to
student feedback, are demonstrated for a sample class. As I smoothed the
transition into each project and guided students through the use of the
technology, their negative feedback on projects decreased and more students
noted how the projects had enhanced their understanding of the course topics.
Best practices learned over the years are given along with project summaries
and sample topics. These projects were implemented at a small liberal arts
university, but advice is given to extend them to larger classes for broader
use.Comment: 27 pages, no figures, 1 tabl
Characterizing Strategic Cascades on Networks
Transmission of disease, spread of information and rumors, adoption of new
products, and many other network phenomena can be fruitfully modeled as
cascading processes, where actions chosen by nodes influence the subsequent
behavior of neighbors in the network graph. Current literature on cascades
tends to assume nodes choose myopically based on the state of choices already
taken by other nodes. We examine the possibility of strategic choice, where
agents representing nodes anticipate the choices of others who have not yet
decided, and take into account their own influence on such choices. Our study
employs the framework of Chierichetti et al. [2012], who (under assumption of
myopic node behavior) investigate the scheduling of node decisions to promote
cascades of product adoptions preferred by the scheduler. We show that when
nodes behave strategically, outcomes can be extremely different. We exhibit
cases where in the strategic setting 100% of agents adopt, but in the myopic
setting only an arbitrarily small epsilon % do. Conversely, we present cases
where in the strategic setting 0% of agents adopt, but in the myopic setting
(100-epsilon)% do, for any constant epsilon > 0. Additionally, we prove some
properties of cascade processes with strategic agents, both in general and for
particular classes of graphs.Comment: To appear in EC 201
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges
Small-world and power-law network structures have been prominently proposed as models of large networks. However, the assumptions of these models usually lack sociological grounding. We present a computational model grounded in social exchange theory. Agents search attractive exchange partners in a diverse population. Agent use simple decision heuristics, based on imperfect, local information. Computer simulations show that the topological structure of the emergent social network depends heavily upon two sets of conditions, harshness of the exchange game and learning capacities of the agents. Further analysis show that a combination of these conditions affects whether star-like, small-world or power-law structures emerge.Complex Networks, Power-Law, Scale-Free, Small-World, Agent-Based Modeling, Social Exchange Theory, Structural Emergence
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