Optimization-Based Influencing of Village Social Networks in a Counterinsurgency

Hung, Benjamin W. K.; Kolitz, Stephan; Ozdaglar, Asuman

doi:10.1007/978-3-642-19656-0_3

Cited by 14 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use a generalized version of ICM similar to [21,13]. The dynamics of the model at each iteration k proceed as follows:…”

Section: Generalized Icmmentioning

confidence: 99%

“…Following previous works [12,13,21], we model the desire dynamic of all agents as a Markov chain where the state of the local neighborhood is a matrix of all existing agents' desire vectors at a particular iteration k and the state transitions are calculated probabilistically from the pairwise interaction between agents connected in a network. The state of the local network around agent i at the k th iteration is a vector of random variables, denoted as X i (k) ∈ R NH i P ×1 (created through a concatenation of N H i vectors of size P ) and expressed as:…”

Section: Him Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Scaling Influence Maximization with Network Abstractions

Maghami

Sukthankar

2014

Lecture Notes in Social Networks

View full text Add to dashboard Cite

Abstract. Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this chapter, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly.

show abstract

“…We use a generalized version of ICM similar to [21,13]. The dynamics of the model at each iteration k proceed as follows:…”

Section: Generalized Icmmentioning

confidence: 99%

Section: Him Algorithmmentioning

confidence: 99%

Scaling Influence Maximization with Network Abstractions

Maghami

Sukthankar

2014

Lecture Notes in Social Networks

View full text Add to dashboard Cite

show abstract

“…Aside from IBM, a number of researchers have also explored mutual maximization models where all players seek to maximize their own influence [23,24]. Finally, Hung et al [25] and Howard [4] also address the COIN problem. However, Hung et al [25] assume a static adversary and Howard [4] only solves for pure strategies.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, Hung et al [25] and Howard [4] also address the COIN problem. However, Hung et al [25] assume a static adversary and Howard [4] only solves for pure strategies. This forced predictability in a repeated-interaction situation is dangerous since a real adversary can directly ambush COIN teams.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Game-Theoretic Target Selection in Contagion-Based Domains

Tsai

Nguyen

Weller

et al. 2013

The Computer Journal

View full text Add to dashboard Cite

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.

show abstract

The method of leader's overthrow in networks based on Shapley value

Belik

Jørnsten

2016

Socio-Economic Planning Sciences

View full text Add to dashboard Cite

Optimization-Based Influencing of Village Social Networks in a Counterinsurgency

Cited by 14 publications

References 7 publications

Scaling Influence Maximization with Network Abstractions

Scaling Influence Maximization with Network Abstractions

Game-Theoretic Target Selection in Contagion-Based Domains

The method of leader's overthrow in networks based on Shapley value

Contact Info

Product

Resources

About