Interacting viruses in networks

Beutel, Alex; Prakash, B. Aditya; Rosenfeld, Roni; Faloutsos, Christos

doi:10.1145/2339530.2339601

Cited by 88 publications

(53 citation statements)

References 38 publications

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“…This is in line with the competitive exclusion principle found in ecology [11,12]. When viruses are not mutually immune, a region can exist where both viruses survive in the network [13]. Even when nodes are mutually immune, there is a region where stronger and/or quicker viruses can coexist with weaker or slower competitors in the SIR model [14].…”

Section: Introductionsupporting

confidence: 80%

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

2014

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When two viruses compete for healthy nodes in a simple network and both spreading rates are above the epidemic threshold, only one virus will survive. However, if we prevent the viruses from dying out, rich dynamics emerge. When both viruses are identical, one virus always dominates the other, but the dominating and dominated virus alternate. We show in the complete graph that the domination time depends on the total number of infected nodes at the beginning of the domination period and, moreover, that the distribution of the domination time decays exponentially yet slowly. When the viruses differ moderately in strength and/or speed the weaker and/or slower virus can still dominate the other but for a short time. Interestingly, depending on the number of infected nodes at the start of a domination period, being quicker can be a disadvantage.

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Section: Introductionsupporting

confidence: 80%

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

2014

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“…The second data set (AA) is an author-author network from DBLP. 4 AA is a co-authorship network, where each node is an author and the existence of an edge indicates the co-authorship between the two corresponding persons. Overall, we have n ¼ 418; 236 nodes and m ¼ 2; 753; 798 edges.…”

Section: Data Setsmentioning

confidence: 99%

“…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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“…In the context of complex network research, the spread of two types of information or epidemic contagions in a network has only recently been considered [18,19,20,21,22,23,24,25]. The earliest study [18] treats two competing epidemics of the susceptible-infective-susceptible (SIS) type, one of which anihilates the other upon contact with certain probability, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In such a setting the authors prove that only one of the epidemics will persist in a network. They later generalize their model to allow coexistence of the two epidemics [24].…”

Section: Introductionmentioning

confidence: 99%

Modeling Information Spreading on Complex Networks

Trpevski¹,

Stamenov²,

Kocarev³

2017

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A b s t r a c t: In this article we propose a model for the spread of two types of information in networks. The model is a natural generalization of the epidemic susceptible-infective-susceptible (SIS) model. The two information types have different attractiveness, which affects the nodes' decision on which information type to adopt when both arrive at a node in the same time step. At difference with results from other authors, the model shows simultaneous existence of the two information types in the stable state. We give approximations for the average number of nodes informed with each information type at the end of the spreading process when nodes have high degree.

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Interacting viruses in networks

Cited by 88 publications

References 38 publications

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

Domination-time dynamics in susceptible-infected-susceptible virus competition on networks

Node Immunization on Large Graphs: Theory and Algorithms

Modeling Information Spreading on Complex Networks

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