Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
Social and Economic Networks
Identifying influential agents for advertising in multi-agent markets
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Hierarchical influence maximization for advertising in multi-agent markets
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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This paper considers the nonlethal targeting assignment problem in the counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We propose two models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counterinsurgents, and 2) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies a strategy for assigning k US agents to produce the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in an experiment the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.