Epidemic Spreading With External Agents

Banerjee, Siddhartha; Gopalan, Aditya; Das, A. K.; Shakkottai, Sanjay

doi:10.1109/tit.2014.2316801

Cited by 23 publications

(23 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As discussed earlier, diffusion models in literature can be broadly classified into: (i) epidemic-based [2][3][4]13,17,24,26] and (ii) gamebased [5,14,23,43], depending on how diffusion occurs, i.e., just like a contagious disease or individuals' strategic choices. In particular, game-based diffusion models [5,14,23,43] adopt a networked coordination game where the payoff matrix appropriately models the value of accepting new technology for the neighbors' selections, and studied the equilibrium and the dynamics.…”

Section: Related Workmentioning

confidence: 99%

“…In [33], it was shown that in highly connected graph, the convergence becomes slower as opposed to in epidemic models. In [21], the authors showed that the external information such as advertisement on a new technology may slow down diffusion, again on the contrary to in epidemic models [4]. In practice, a small set of influential nodes, called seeds, can be convinced to pre-adopt a new technology, which can increase the effect of diffusion.…”

Section: Related Workmentioning

confidence: 99%

“…3 In fact, the author [22] considers polynomially-growing graphs and minor-excluded graphs, where d-dimensional graphs and planar graphs are their special cases, respectively. 4 It is a polynomial with respect to n, but may be exponential with respect to 1/ε. Input: Graph G = (V, E) and seed budget k Output: Seed set C PaS 1.…”

Section: Geometrically Structured Graphsmentioning

confidence: 99%

See 2 more Smart Citations

On maximizing diffusion speed in social networks

Jin

Shin

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

View full text Add to dashboard Cite

A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We study how diffusion effect can be maximized by seeding a subset of individuals (within a given budget), i.e., convincing them to pre-adopt a new innovation. In particular, we aim at finding 'good' seeds for minimizing the time to infect all others, i.e., diffusion speed maximization. To this end, we design polynomial-time approximation algorithms for three representative classes, Erdős-Rényi, planted partition and geometrically structured graph models, which correspond to globally well-connected, locally well-connected with large clusters and locally well-connected with small clusters, respectively, provide their performance guarantee in terms of approximation and complexity. First, for the dense Erdős-Rényi and planted partition graphs, we show that an arbitrary seeding and a simple seeding proportional to the size of clusters are almost optimal with high probability. Second, for geometrically structured sparse graphs, including planar and d-dimensional graphs, our algorithm that (a) constructs clusters, (b) seeds the border individuals among clusters, and (c) greedily seeds inside each cluster always outputs an almost optimal solution. We validate our theoretical findings with extensive simulations under a real social graph. We believe that our results provide new practical insights on how to seed over a social network depending on its connection structure, where individuals rationally adopt a new innovation. To our best knowledge, we are the first to study such diffusion speed maximization on the game-based diffusion, while the extensive research efforts have been made in epidemicbased models, often referred to as influence maximization.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Geometrically Structured Graphsmentioning

confidence: 99%

See 1 more Smart Citation

On maximizing diffusion speed in social networks

Jin

Shin

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

View full text Add to dashboard Cite

show abstract

“…Specifically, there is a vast literature to characterize spreading time in various contexts for SI processes [1,2,3,4], and phase transitions/extinction time for SIS processes [6,7,8,9,10,11]. Phase transitions for SIR processes are available in [13].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the spreading-time is stochastically upper bounded by the maximum of n i.i.d random variables distributed as sum of diam(G) number of exponential random variables with rate β. Finally, using standard concentration results for the sum of exponentials (refer [3]…”

Section: Si − Si Bs Dynamicsmentioning

confidence: 99%

The behavior of epidemics under bounded susceptibility

Krishnasamy

Banerjee

Shakkottai

2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

Self Cite

View full text Add to dashboard Cite

We investigate the sensitivity of epidemic behavior to a bounded susceptibility constraint -susceptible nodes are infected by their neighbors via the regular SI/SIS dynamics, but subject to a cap on the infection rate. Such a constraint is motivated by modern social networks, wherein messages are broadcast to all neighbors, but attention spans are limited. Bounded susceptibility also arises in distributed computing applications with download bandwidth constraints, and in human epidemics under quarantine policies.Network epidemics have been extensively studied in literature; prior work characterizes the graph structures required to ensure fast spreading under the SI dynamics, and long lifetime under the SIS dynamics. In particular, these conditions turn out to be meaningful for two classes of networks of practical relevance -dense, uniform (i.e., cliquelike) graphs, and sparse, structured (i.e., star-like) graphs. We show that bounded susceptibility has a surprising impact on epidemic behavior in these graph families. For the SI dynamics, bounded susceptibility has no effect on starlike networks, but dramatically alters the spreading time in clique-like networks. In contrast, for the SIS dynamics, clique-like networks are unaffected, but star-like networks exhibit a sharp change in extinction times under bounded susceptibility.Our findings are useful for the design of disease-resistant networks and infrastructure networks. More generally, they show that results for existing epidemic models are sensitive to modeling assumptions in non-intuitive ways, and suggest caution in directly using these as guidelines for real systems.

show abstract

Burning a Graph as a Model of Social Contagion

Bonato

Janssen

Roshanbin

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We introduce a new graph parameter called the burning number, inspired by contact processes on graphs such as graph bootstrap percolation, and graph searching paradigms such as Firefighter. The burning number measures the speed of the spread of contagion in a graph; the lower the burning number, the faster the contagion spreads. We provide a number of properties of the burning number, including characterizations and bounds. The burning number is computed for several graph classes, and is derived for the graphs generated by the Iterated Local Transitivity model for social networks.

show abstract

Epidemic Spreading With External Agents

Cited by 23 publications

References 44 publications

On maximizing diffusion speed in social networks

On maximizing diffusion speed in social networks

The behavior of epidemics under bounded susceptibility

Burning a Graph as a Model of Social Contagion

Contact Info

Product

Resources

About