A note on maximizing the spread of influence in social networks

TL;DR: The key contribution of this work is the introduction of memory into the navigation process, by making user conversion dependent on the number of her exposures to that content, and a general and efficient algorithmic solution is proposed.

TL;DR: It is shown that a critical threshold vector can be translated to a graph coloring, based on which a necessary condition for influence maximization is established, which can be used to find a large family of critical threshold vectors.

TL;DR: Wang et al. as mentioned in this paper proposed a methodology to extract events from noisy web messages automatically, based on the essence and the propagation features of event ontologies, they analyzed the correlation between three typical kinds of public security incidents in China.

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.