Finding Spread Blockers in Dynamic Networks

Habiba, Habiba; Yu, Yi; Berger-Wolf, Tanya Y.; Saia, Jared

doi:10.1007/978-3-642-14929-0_4

Cited by 60 publications

(36 citation statements)

References 54 publications

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“…Our approach, in contrast, is focused on t ≥ 2 and our heuristics compute a critical set for any specified set of seed nodes. Critical nodes are called "blockers" in [18]. They examine dynamic networks and use a probabilistic diffusion model with threshold = 1.…”

Section: Summary Of Results and Related Workmentioning

confidence: 99%

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Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

Kuhlman

Kumar

Marathe

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Abstract.We study the problem of inhibiting diffusion of complex contagions such as rumors, undesirable fads and mob behavior in social networks by removing a small number of nodes (called critical nodes) from the network. We show that, in general, for any ρ ≥ 1, even obtaining a ρ-approximate solution to these problems is NP-hard. We develop efficient heuristics for these problems and carry out an empirical study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that the heuristics perform well on the three social networks.

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Section: Summary Of Results and Related Workmentioning

confidence: 99%

“…Other formulations of this problem have been considered in the literature for simple contagions (e.g. [18]). We will discuss the differences between our work and that reported in other references in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

Kuhlman

Kumar

Marathe

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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“…However, the majority of this work focuses on dynamic patterns [10,17,16,22,27], temporal link prediction [8], anomaly detection [1], dynamic communities [18,28,11], dynamic ranking [20,24], and many others [31,12].…”

Section: Related Workmentioning

confidence: 99%

“…Although some recent research has focused on the analysis of dynamic networks [17,3,5,12,4,21], there has been less work on developing models of temporal behavior in large scale network datasets. There has been some work on modeling temporal events in large scale networks [2,31] and other work that uses temporal link and attribute patterns to improve predictive models [26].…”

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confidence: 99%

Modeling dynamic behavior in large evolving graphs

Rossi

Gallagher

Neville

et al. 2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

143

116

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Given a large time-evolving graph, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the "roles" of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/datadriven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable and computationally efficient. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

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“…These games typically model nature as the adversary, which chooses an initial set of nodes with some predefined probability distribution that the defender is optimizing against [17][18][19][20][21][22]. Variations on this include distributed inoculation games where each node acts independently, in which results such as price of anarchy are generally considered [17,20].…”

Section: Related Workmentioning

confidence: 99%

Game-Theoretic Target Selection in Contagion-Based Domains

Tsai

Nguyen

Weller

et al. 2013

The Computer Journal

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Many strategic actions carry a 'contagious'component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, we combine recent research in influence-blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks and scale-free graphs. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected scale-free graphs.

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Finding Spread Blockers in Dynamic Networks

Cited by 60 publications

References 54 publications

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

Modeling dynamic behavior in large evolving graphs

Game-Theoretic Target Selection in Contagion-Based Domains

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