Competing Memes Propagation on Networks: A Network Science Perspective

Wei, Xuetao; Valler, Nicholas C.; Prakash, B. Aditya; Neamtiu, Iulian; Faloutsos, Michalis; Faloutsos, Christos

doi:10.1109/jsac.2013.130607

Cited by 101 publications

(81 citation statements)

References 32 publications

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“…Therefore, the underlying network of infections has two partites connected together: one comprised of mosquitoes and the other of human beings. Therefore, such ODEs conform to a natural model to study the evolution of the fraction of infected nodes at each partite on a large-scale complete-multipartite network (from [7]) as in Dengue-like epidemics, or to study the evolution of the likelihood of infection of nodes in an arbitrary network as, e.g., in [5], [6] for singlevirus, or [9] for bi-virus.…”

Section: Introduction and Brief Review Of Thementioning

confidence: 99%

“…The analytic study of this dynamical system can inform the regulatory policies of vaccination to preventing the persistence of the epidemics. Similar models for bi-virus competition in a network are addressed in references [9], [17], [18]. The common feature among these papers and the majority of the literature in epidemics is that they perform local analysis -i.e., determine properties of the equilibria, or study the dynamical system on small neighborhoods of the equilibrium points.…”

Section: Introduction and Brief Review Of Thementioning

confidence: 99%

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Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

Santos

Moura

2015

IEEE Trans. Netw. Sci. Eng.

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Abstract-The paper studies the qualitative behavior of a set of Ordinary Differential Equations that models the dynamics of bi-virus epidemics over bilayer networks. Each layer is a weighted digraph associated with a strain of virus; the weights γ z ij represent the rates of infection from node i to node j of strain z. We establish a sufficient condition on the γ's that guarantees survival of the fittest-only one strain survives. We propose an ordering of the weighted digraphs, the -order, and show that if the weighted digraph of strain y is -dominated by the weighted digraph of strain x, then y dies out in the long run. We prove that the orbits of the ODE accumulate to an attractor. Due to the coupled nonlinear high-dimension nature of the ODEs, there is no natural Lyapunov function to study their global qualitative behavior. We prove our results by combining two important properties of these ODEs: (i) monotonicity under a partial ordering on the set of graphs; and (ii) dimension-reduction under symmetry of the graphs. Property (ii) allows us to fully address the survival of the fittest for regular graphs. Then, by bounding the epidemics dynamics for generic networks by the dynamics on regular networks, we prove the result for general networks.

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Section: Introduction and Brief Review Of Thementioning

confidence: 99%

Section: Introduction and Brief Review Of Thementioning

confidence: 99%

Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

Santos

Moura

2015

IEEE Trans. Netw. Sci. Eng.

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“…A large amount of work is also done on analyzing the spreading process of competing information, virus and etc. [4], [59], [62]. The algorithm in [23] enables within-network and across-network classification with regional features of the graph.…”

Section: Related Workmentioning

confidence: 99%

Node Immunization on Large Graphs: Theory and Algorithms

Chen

Tong

Prakash

et al. 2016

IEEE Trans. Knowl. Data Eng.

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Abstract-Given a large graph, like a computer communication network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, referred to as the node immunization problem, is the core building block in many high-impact applications, ranging from public health, cybersecurity to viral marketing. A central component in node immunization is to find the best k bridges of a given graph. In this setting, we typically want to determine the relative importance of a node (or a set of nodes) within the graph, for example, how valuable (as a bridge) a person or a group of persons is in a social network. First of all, we propose a novel 'bridging' score D, inspired by immunology, and we show that its results agree with intuition for several realistic settings. Since the straightforward way to compute D is computationally intractable, we then focus on the computational issues and propose a surprisingly efficient way (Oðnk 2 þ mÞ) to estimate it. Experimental results on real graphs show that (1) the proposed 'bridging' score gives mining results consistent with intuition; and (2) the proposed fast solution is up to seven orders of magnitude faster than straightforward alternatives.

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“…Finally, we should also point out that the case of co-operating epidemics is very much different from the case of competing or antagonistic epidemics [23][24][25][26][27][28][29][30][31][32]. Although the latter are also of huge practical interest, their dynamics is very different and leads in general to less dramatic effects.…”

Section: Introductionmentioning

confidence: 99%

Cooperative epidemics on multiplex networks

Azimi-Tafreshi

2016

Phys. Rev. E

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The spread of one disease, in some cases, can stimulate the spreading of another infectious disease. Here, we treat analytically a symmetric co-infection model for spreading of two diseases on a twolayer multiplex network. We allow layer overlapping, but we assume that each layer is random and locally loop-less. Infection with one of the diseases increases the probability of getting infected with the other. Using the generating function method, we calculate exactly the fraction of individuals infected with both diseases (so-called co-infected clusters) in the stationary state, as well as the epidemic spreading thresholds and the phase diagram of the model. With increasing cooperation, we observe a tricritical point and the type of transition changes from continuous to hybrid. Finally, we compare the co-infected clusters in the case of co-operating diseases with the so-called "viable" clusters in networks with dependencies.

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Competing Memes Propagation on Networks: A Network Science Perspective

Cited by 101 publications

References 32 publications

Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

Node Immunization on Large Graphs: Theory and Algorithms

Cooperative epidemics on multiplex networks

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