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Given a directed graph (representing a social network), the influence maximization problem is to find k nodes which, when influenced (or activated), would maximize the number of remaining nodes that get activated. In this paper, we consider a more general version of the problem that includes an additional set of nodes, termed as physical nodes, such that a node in the social network is covered by one or more physical nodes. A physical node exists in one of two states at any time, opened or closed, and there is a constraint on the maximum number of physical nodes that can be opened. In this setting, an inactive node in the social network becomes active if it has enough active neighbors in the social network and if it is covered by at least one of the opened physical nodes. This problem arises in disaster recovery, where a displaced social group decides to return after a disaster only after enough groups in its social network return and some infrastructure components in its neighborhood are repaired. The general problem is NP-hard to approximate within any constant factor and thus we characterize optimal and approximation algorithms for special instances of the problem.
Influence Maximization (IM) aims to maximize the number of people that become aware of a product by finding the `best set of `seed users to initiate the product advertisement. Unlike prior arts on static social networks containing fixed number of use
Social networks have been popular platforms for information propagation. An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample pr
Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to users privacy concern, overwhelming network scale etc., it is hard to target any user in
Uncertainty about models and data is ubiquitous in the computational social sciences, and it creates a need for robust social network algorithms, which can simultaneously provide guarantees across a spectrum of models and parameter settings. We begin
Influence Maximization is a NP-hard problem of selecting the optimal set of influencers in a network. Here, we propose two new approaches to influence maximization based on two very different metrics. The first metric, termed Balanced Index (BI), is