The Social Medium Selection Game

We consider in this paper competition of content creators in routing their content through various media. The routing decisions may correspond to the selection of a social network (e.g. twitter versus facebook or linkedin) or of a group within a given social network. The utility for a player to send its content to some medium is given as the difference between the dissemination utility at this medium and some transmission cost. We model this game as a congestion game and compute the pure potential of the game. In contrast to the continuous case, we show that there may be various equilibria. We show that the potential is M-concave which allows us to characterize the equilibria and to propose an algorithm for computing it. We then give a learning mechanism which allow us to give an efficient algorithm to determine an equilibrium. We finally determine the asymptotic form of the equilibrium and discuss the implications on the social medium selection problem

Rumour Source Detection Using Game Theory

Social networks have become a critical part of our lives as they enable us to interact with a lot of people. These networks have become the main sources for creating, sharing and also extracting information regarding various subjects. But all this information may not be true and may contain a lot of unverified rumours that have the potential of spreading incorrect information to the masses, which may even lead to situations of widespread panic. Thus, it is of great importance to identify those nodes and edges that play a crucial role in a network in order to find the most influential sources of rumour spreading. Generally, the basic idea is to classify the nodes and edges in a network with the highest criticality. Most of the existing work regarding the same focuses on using simple centrality measures which focus on the individual contribution of a node in a network. Game-theoretic approaches such as Shapley Value (SV) algorithms suggest that individual marginal contribution should be measured for a given player as the weighted average marginal increase in the yield of any coalition that this player might join. For our experiment, we have played five SV-based games to find the top 10 most influential nodes on three network datasets (Enron, USAir97 and Les Misérables). We have compared our results to the ones obtained by using primitive centrality measures. Our results show that SVbased approach is better at understanding the marginal contribution, and therefore the actual influence, of each node to the entire network