Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to nonfederal websites. Their policies may differ from this site.

We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of infected nodes subject to a budget constraint on the total number of nodes that can be vaccinated. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. Since the optimization problem is NPhard, we develop a heuristic based on a generalization of the set cover problem. Using experiments on three realworld networks, we compare the performance of the heuristic with some baseline methods.

We investigate questions related to the time evolution of discrete graph dynamical systems where each node has a state from {0,1}. The configuration of a system at any time instant is a Boolean vector that specifies the state of each node at that instant. We say that two configurations are similar if the Hamming distance between them is small. Also, a predecessor of a configuration B is a configuration A such that B can be reached in one step from A. We study problems related to the similarity of predecessor configurations from which two similar configurations can be reached in one time step. We address these problems both analytically and experimentally. Our analytical results point out that the level of similarity between predecessors of two similar configurations depends on the local functions of the dynamical system. Our experimental results, which consider random graphs as well as small world networks, rely on the fact that the problem of finding predecessors can be reduced to the Boolean Satisfiability problem (SAT).

Discrete graphical dynamical systems serve as effective formal models in many contexts, including simulations of agentbased models, propagation of contagions in social networks and study of biological phenomena. A class of Boolean functions, called nested canalyzing functions (NCFs), has been used as a good model of certain biological phenomena. Motivated by these biological applications, we study a variety of analysis problems for synchronous graphical dynamical systems (SyDSs) over the Boolean domain, where each local function is an NCF. Each analysis problem involves testing whether the phase space of a given SyDS satisfies a certain property. We present intractability results for some properties as well as efficient algorithms for others. In several cases, our results clearly delineate intractable and efficiently solvable versions of problems

Nested canalyzing functions (NCFs) are a class of Boolean functions which are used to model certain biological phenomena. We derive a complete characterization of NCFs with the largest average sensitivity, expressed in terms of a simple structural property of the NCF. This characterization provides an alternate, but elementary, proof of the tight upper bound on the average sensitivity of any NCF established by Klotz et al. (2013). We also utilize the characterization to derive a closed form expression for the number of NCFs that have the largest average sensitivity.

Using synchronous dynamical systems (SyDSs) as a formal model for networked social systems, we study the problem of inferring users’ choices in such systems. We observe that SyDSs with deterministic and probabilistic threshold functions as local functions can capture users’ choices in the context of contagion propagation in social networks. We use an active query mechanism where a user interacts with a system by submitting queries, and the responses to the queries are used to infer the local functions. We develop methods that provide provably efficient query sets for inferring both deterministic and probabilistic forms of threshold functions. We also present experimental results using real world social networks.