Threshold conditions for arbitrary cascade models on arbitrary networks

Prakash, B. Aditya; Chakrabarti, Deepayan; Valler, Nicholas C.; Faloutsos, Michalis; Faloutsos, Christos

doi:10.1007/s10115-012-0520-y

Cited by 127 publications

(87 citation statements)

References 53 publications

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“…Intuitively, we say that a meme dominates over the other meme if it manages to capture more nodes. The definition hides several subtleties, which have to do with the asymptotic behavior of the system, namely what happens as time goes to infinity [12]. However, as we are reliant on simulations, we are forced to adopt a more practical definition.…”

Section: B Problem Definitionsmentioning

confidence: 99%

“…Ganesh et al [13] and Yang et al [11] provided epidemic threshold for the single-virus on single topology. Prakash et al [12] gave the epidemic threshold condition for almost all single-virus epidemic models on a single static network. Cohen et al [21] studied the well-known acquaintance immunization method and showed that it is much better than random methods.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, most previous efforts either studied a single epidemic on a single topology [8], [9], [10], [11], [12], [13], [3], or studied two pathogens, but on the same topology, and under the assumption that the two viruses appear one after the other [14]. More recent work has studied the quarantining techniques in the case of computer viruses [15], but it has not attempted to study the problem analytically or derive the 0733-8716/13/$31.00 c 2013 IEEE conditions that predict the outcome in the composite networks.…”

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confidence: 99%

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Competing Memes Propagation on Networks: A Network Science Perspective

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Valler

Prakash

et al. 2013

IEEE J. Select. Areas Commun.

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101

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Abstract-In this paper, we study the intertwined propagation of two competing "memes" (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI1I2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected.

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Section: B Problem Definitionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Competing Memes Propagation on Networks: A Network Science Perspective

Wei

Valler

Prakash

et al. 2013

IEEE J. Select. Areas Commun.

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101

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“…To quantify the overall full-stream effect, intuitively, when edges are merged, our goal is to maintain the diffusive property of the whole graph G . Prakash et al [20] demonstrate that the diffusive property of a graph is captured by the largest eigenvalue of the adjacency matrix of a graph, for a wide range of cascade style propagation models, including IC model. We adapt this methodology in this paper 2 .…”

Section: Finding Media Nodesmentioning

confidence: 99%

MeiKe: Influence-based Communities in Networks

Zhang

Adhikari

Jan

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Given a social network, how to find communities of nodes based on their diffusive characteristics? There exist two important types of nodes, for information propagation: nodes that are influential ("kernel nodes"), and nodes that serve as "bridges" to boost the diffusion ("media nodes"). How to find these nodes and uncover connections between them? In addition, it is also important to discover the hidden community structure of these nodes, which can help study their interactions, predict links and also understand the information flow in such networks.In this paper, we give an intuitive and novel optimization-based formulation for this task, which aims to discover media nodes as well as community structures of kernel nodes. We prove our task is computationally challenging, and develop an effective and practical algorithm MeiKe (pronounced as 'Mike'). It first obtains media nodes via a new successive summarization based approach, and then finds kernel nodes including their community structures. Experimental results show that MeiKe finds high-quality media and kernel communities which match our expectations and ground-truth (outperforming non-trivial baselines by 40% in F1-score). Our case studies also demonstrate the applicability of MeiKe on a variety of datasets.

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“…al. [23] further discovered that the leading eigenvalue and a modeldependent constant are the only parameters that determine the epidemic threshold for almost all virus propagation models. Prakash et.…”

Section: Theorymentioning

confidence: 99%

Understanding and managing cascades on large graphs

Prakash

Faloutsos

2012

Proc. VLDB Endow.

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How do contagions spread in population networks? Which group should we market to, for maximizing product penetration? Will a given YouTube video go viral? Who are the best people to vaccinate? What happens when two products compete? The objective of this tutorial is to provide an intuitive and concise overview of most important theoretical results and algorithms to help us understand and manipulate such propagation-style processes on large networks. The tutorial contains three parts: (a) Theoretical results on the behavior of fundamental models; (b) Scalable Algorithms for changing the behavior of these processes e.g., for immunization, marketing etc.; and (c) Empirical Studies of diffusion on blogs and on-line websites like Twitter.The problems we focus on are central in surprisingly diverse areas: from computer science and engineering, epidemiology and public health, product marketing to information dissemination. Our emphasis is on intuition behind each topic, and guidelines for the practitioner.

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Threshold conditions for arbitrary cascade models on arbitrary networks

Cited by 127 publications

References 53 publications

Competing Memes Propagation on Networks: A Network Science Perspective

Competing Memes Propagation on Networks: A Network Science Perspective

MeiKe: Influence-based Communities in Networks

Understanding and managing cascades on large graphs

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