A note on maximizing the spread of influence in social networks

TL;DR: In this article , the authors generalize the biased voter model to the positional voter model, in which the invasion bias is effectuated only on an arbitrary subset of the network nodes, called biased nodes.

TL;DR: A novel variant of EANN is proposed by adopting characteristics of a well-performing heuristic into the structural bias of the neurocontroller, which demonstrates improvements in both solution quality and execution time.

TL;DR: A new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes, and extensive experiments show this method has a good performance in different social networks.

TL;DR: Influence Maximization on Families of Graphs shows that the influence of a graph’s graph-like properties has an important effect on the size of the graph itself.

TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.

TL;DR: The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.

TL;DR: This article developed models of collective behavior for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative, and the key...

TL;DR: Social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals achieve their goals.