Results 21  30
of
40
Finding NearOptimal Groups of Epidemic Spreaders in a Complex Network
, 2013
"... In this paper, we present algorithms to find nearoptimal sets of epidemic spreaders in complex networks. We extend the notion of localcentrality, a centrality measure previously shown to correspond with a node’s ability to spread an epidemic, to sets of nodes by introducing combinatorial local cen ..."
Abstract
 Add to MetaCart
In this paper, we present algorithms to find nearoptimal sets of epidemic spreaders in complex networks. We extend the notion of localcentrality, a centrality measure previously shown to correspond with a node’s ability to spread an epidemic, to sets of nodes by introducing combinatorial local centrality. Though we prove that finding a set of nodes that maximizes this new measure is NPhard, good approximations are available. We show that a strictly greedy approach obtains the best approximation ratio unless P =NP and then formulate a modified version of this approach that leverages qualities of the network to achieve a faster runtime while maintaining this theoretical guarantee. We perform an experimental evaluation on samples from several different network structures which demonstrate that our algorithm maximizes combinatorial local centrality and consistently chooses the most effective set of nodes to spread infection under the SIR model, relative to selecting the top nodes using many common centrality measures. We also demonstrate that the optimized algorithm we develop scales effectively.
A Using Generalized Annotated Programs to Solve Social Network Diffusion Optimization Problems
"... There has been extensive work in many different fields on how phenomena of interest (e.g. diseases, innovation, product adoption) “diffuse ” through a social network. As social networks increasingly become a fabric of society, there is a need to make “optimal ” decisions with respect to an observed ..."
Abstract
 Add to MetaCart
There has been extensive work in many different fields on how phenomena of interest (e.g. diseases, innovation, product adoption) “diffuse ” through a social network. As social networks increasingly become a fabric of society, there is a need to make “optimal ” decisions with respect to an observed model of diffusion. For example, in epidemiology, officials want to find a set of k individuals in a social network which, if treated, would minimize spread of a disease. In marketing, campaign managers try to identify a set of k customers that, if given a free sample, would generate maximal “buzz ” about the product. In this paper, we first show that the wellknown Generalized Annotated Program (GAP) paradigm can be used to express many existing diffusion models. We then define a class of problems called Social Network Diffusion Optimization Problems (SNDOPs). SNDOPs have four parts: (i) a diffusion model expressed as a GAP, (ii) an objective function we want to optimize with respect to a given diffusion model, (iii) an integer k> 0 describing resources (e.g. medication) that can be placed at nodes, (iv) a logical condition V C that governs which nodes can have a resource (e.g. only children above the age of 5 can be treated with a given medication). We study the computational complexity of SNDOPs and show both NPcompleteness results as well as results on complexity of approximation. We then develop an exact and a heuristic algorithm to solve a large class of SNDOP problems and show that our GREEDYSNDOP algorithm achieves the best possible approximation ratio that a polynomial
A novel analytical method for evolutionary graph theory problems
, 2013
"... Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general di ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide nontrivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a nontrivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.
COMPARATIVE ANALYSIS OF CYBERATTACKS ON
"... The rapid informatization of the world which has started since the beginning of 90s led to the growing state interdependence from cyberspace. The Internet has become crucial to the society, economy, military of contemporary country. This situation became a new challenge for the national security and ..."
Abstract
 Add to MetaCart
The rapid informatization of the world which has started since the beginning of 90s led to the growing state interdependence from cyberspace. The Internet has become crucial to the society, economy, military of contemporary country. This situation became a new challenge for the national security and more and more often the term cyberwar has been used. Despite the fact that this phenomenon is not clearly defined the massive cyberattack on countries took place in the past. The main aim of this article is to examine three cases of these attacks: on Estonia in 2007, on Georgia in 2008 and on Kyrygysytan 2009 and to try finding similarities and differences and answer the question who carried out these strikes and why. In order to do it the following factors will be analyzed: the political background of these countries and the relation with the neighbours, the time and scale of attacks and effect of them. In conclusion the article tries to find the most difficult answer who was a perpetrator. The three hypotheses were presented with evaluation of probability of them.
Military Operational Art and
"... rha.lindelauf.01[at]nlda.nl An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has t ..."
Abstract
 Add to MetaCart
rha.lindelauf.01[at]nlda.nl An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has the capability to launch cyberattacks as practical concerns (i.e. avoiding disruption of service, presence of legacy systems, etc.) may hinder security. Hence, a defender can harden the security posture at certain power stations but may lack the time and resources to do this for the entire power grid. We model a power grid as a graph and introduce the cascading failure game in which both the defender and attacker choose a subset of power stations such as to minimize (maximize) the number of consumers having access to producers of power. We formalize problems for identifying both mixed and deterministic strategies for both players, prove complexity results under a variety of different scenarios, identify tractable cases, and develop algorithms for these problems. We also perform an experimental evaluation of the model and game on a realworld power grid network. Empirically, we noted that the game favors the attacker as he benefits more from increased resources than the defender. Further, the minimax defense produces roughly the same expected payoff as an easytocompute deterministic load based (DLB) defense when played against a minimax attack strategy. However, DLB performs more poorly than minimax defense when faced with the attacker’s best response to DLB. This is likely due to the presence of lowload yet highpayoff nodes, which we also found in our empirical analysis.
ABSTRACT Title of dissertation: EVOLUTIONARY GAME THEORETIC MODELING OF DECISION MAKING AND CULTURE
"... Evolutionary Game Theory (EGT) has become an attractive framework for modeling human behavior because it provides tools to explicitly model the dynamics of behaviors in populations over time and does not require the strong rationality assumptions of classical game theory. Since the application of E ..."
Abstract
 Add to MetaCart
Evolutionary Game Theory (EGT) has become an attractive framework for modeling human behavior because it provides tools to explicitly model the dynamics of behaviors in populations over time and does not require the strong rationality assumptions of classical game theory. Since the application of EGT to human behavior is still relatively new, many questions about human behavior and culture of interest to social scientists have yet to be examined through an EGT perspective to determine whether explanatory and predictive rather than merely descriptive insights can be gained. In this thesis, informed by social science data and under close collaboration with social scientists, I use EGTbased approaches to model and gain a qualitative understanding of various aspects of the evolution of human decisionmaking and culture. The specific phenomena I explore are i) risk preferences and their implications on the evolution of cooperation and ii) the relationship between societal threat and the propensity with which agents of societies punish normviolating behavior. First, inspired by much empirical research that shows human riskpreferences
Abstract Title of dissertation: Stochastic Reasoning with Action Probabilistic Logic Programs
"... In the real world, there is a constant need to reason about the behavior of various entities. A soccer goalie could benefit from information available about past penalty kicks by the same player facing him now. National security experts could benefit from the ability to reason about behaviors of ter ..."
Abstract
 Add to MetaCart
In the real world, there is a constant need to reason about the behavior of various entities. A soccer goalie could benefit from information available about past penalty kicks by the same player facing him now. National security experts could benefit from the ability to reason about behaviors of terror groups. By applying behavioral models, an organization may get a better understanding about how best to target their efforts and achieve their goals. In this thesis, we propose action probabilistic logic (or ap) programs, a formalism designed for reasoning about the probability of events whose interdependencies are unknown. We investigate how to use apprograms to reason in the kinds of scenarios described above. Our approach is based on probabilistic logic programming, a well known formalism for reasoning under uncertainty, which has been shown to be highly flexible since it allows imprecise probabilities to be specified in the form of intervals that convey the inherent uncertainty in the knowledge. Furthermore, no independence assumptions are made, in contrast to many of the probabilistic reasoning formalisms that have been proposed. Up to now, all work in probabilis
Results 21  30
of
40