This paper looks at reliability with a new goal: development of a multicast protocol which is reliable in a sense that can be rigorously quantified and includes throughput stability guarantees. We characterize this new protocol as a "bimodal multicast" in reference to its reliability model, which corresponds to a family of bimodal probability distributions. Here, we introduce the protocol, provide a theoretical analysis of its behavior, review experimental results, and discuss some candidate applications. These confirm that bimodal multicast is reliable, scalable, and that the protocol provides remarkably stable delivery throughput

Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc. We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized socialnetwork graphs. To demonstrate its effectiveness on realworld networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12 % error rate. Our de-anonymization algorithm is based purely on the network topology, does not require creation of a large number of dummy “sybil ” nodes, is robust to noise and all existing defenses, and works even when the overlap between the target network and the adversary’s auxiliary information is small. 1.

A Master equation approach to the stochastic neurodynamics proposed by Cowan[ in Advances in Neural Information Processing Systems 3, edited by R.P. Lippman, J.E. Moody, and D.S. Touretzky (Morgan Kaufmann, San Mateo, 1991), p.62] is investigated in this paper. We deal with a model neural network which is composed of two-state neurons obeying elementary stochastic transition rates. We show that such an approach yields concise expressions for multi-point moments and an equation of motion. We apply the formalism to an (1+1)-dimensional system. Exact and approximate expressions for various statistical parameters are obtained and compared with Monte Carlo simulations. I. INTRODUCTION Recently, Cowan introduced a model to describe stochastic neural networks [1] based on a master equation[2], which he called the "Neural Network Master Equation" (NNME). The purpose of this paper is to investigate some of the properties of this equation for the case of networks composed of two-state neurons...

Abstract—This paper proposes the use of methods for network analysis in order to study the properties of a dynamic graph that model the interaction among agents in an agent-based model. This model is based on Multi Agent System definition and simulates a multicultural crowd in which proxemics theory and distance perception are taking into account. After a discussion about complex network analysis and crowd research context, an agent-based model based on SCA*PED (Situated Cellular Agents for PEdestrian Dynamics) approach is presented, based on two separated yet interconnected layers representing different aspects of the overall system dynamics. Then, an analysis of network derived from agent interactions in the Proxemic layer is proposed, identifying characteristic structures and their meaning in the crowd analysis. At the end an analysis related to the identification of those characteristic structures in some real examples is proposed. I.

bioterror response logistics: the case of smallpox

Network epidemics and early stage vaccination: the effects of infectious and vaccination delay periods and their randomness

We study the ABC model (A + B → 2B, B + C → 2C, C + A → 2A), and its counterpart: the three–component neutral drift model (A + B → 2A or 2B, B + C → 2B or 2C, C + A → 2C or 2A.) In the former case, the mean field approximation exhibits cyclic behaviour with an amplitude determined by the initial condition. When stochastic phenomena are taken into account the amplitude of oscillations will drift and eventually one and then two of the three species will become extinct. The second model remains stationary for all initial conditions in the mean field approximation, and drifts when stochastic phenomena are considered. We analyzed the distribution of first extinction times of both models by simulations and from the point of view of the Fokker-Planck equation. Survival probability vs. time plots suggest an exponential decay. For the neutral model the extinction rate is inversely proportional to the system size, while the cyclic model exhibits anomalous behaviour for small system sizes. In the large system size limit the extinction times for both models will be the same. This result is compatible with the smallest eigenvalue obtained from the numerical solution of the Fokker-Planck equation. We also studied the long–time behaviour of the probability distribution. The exponential decay is found to be robust against certain changes, such as the three reactions having different rates. 0 1

modeling in metapopulation systems with heterogeneous coupling pattern:

We present a modified susceptible-infected-susceptible (SIS) model on complex networks, small-world and scale-free, to study epidemic spreading with the effect of time delay which is introduced to the infected phase. Considering the topology of the network, both uniform and degree-dependent delays are studied during the contagion process. We find that the existence of time delay will enhance both outbreaks and prevalence of infectious diseases in the network.