Piotr L. Szczepański

Efficient computation of the shapley value for game-theoretic network centrality

The Shapley value--probably the most important normative payoff division scheme in coalitional games--has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.

A centrality measure for networks with community structure based on a generalization of the owen value

There is currently much interest in the problem of measuring the centrality of nodes in networks/graphs; such measures have a range of applications, from social network analysis, to chemistry and biology. In this paper we propose the first measure of node centrality that takes into account the community structure of the underlying network. Our measure builds upon the recent literature on game-theoretic centralities, where solution concepts from cooperative game theory are used to reason about importance of nodes in the network. To allow for flexible modelling of community structures, we propose a generalization of the Owen value—a well-known solution concept from cooperative game theory to study games with a priori-given unions of players. As a result we obtain the first measure of centrality that accounts for both the value of an individual nodes relationships within the network and the quality of the community this node belongs to.

Implementation and Computation of a Value for Generalized Characteristic Function Games

Generalized characteristic function games are a variation of characteristic function games, in which the value of a coalition depends not only on the identities of its members, but also on the order in which the coalition is formed. This class of games is a useful abstraction for a number of realistic settings and economic situations, such as modeling relationships in social networks. To date, two main extensions of the Shapley value have been proposed for generalized characteristic function games: the Nowak-Radzik [1994] value and the Sánchez-Bergantiños [1997] value. In this context, the present article studies generalized characteristic function games from the point of view of implementation and computation. Specifically, the article makes two key contributions. First, building upon the mechanism by Dasgupta and Chiu [1998], we present a non-cooperative mechanism that implements both the Nowak-Radzik value and the Sánchez-Bergantiños value in Subgame-Perfect Nash Equilibria in expectations. Second, in order to facilitate an efficient computation supporting the implementation mechanism, we propose the Generalized Marginal-Contribution Nets representation for this type of game. This representation extends the results of Ieong and Shoham [2005] and Elkind et al. [2009] for characteristic function games and retains their attractive computational properties.

Efficient algorithms for game-theoretic betweenness centrality

Betweenness centrality measures the ability of different nodes to control the flow of information in a network. In this article, we extend the standard definition of betweenness centrality using Semivalues-a family of solution concepts from cooperative game theory that includes, among others, the Shapley value and the Banzhaf power index. Any Semivalue-based betweenness centrality measure (such as, for example, the Shapley value-based betweenness centrality measure) has the advantage of evaluating the importance of individual nodes by considering the roles they each play in different groups of nodes. Our key result is the development of a general polynomial-time algorithm to compute the Semivalue-based betweenness centrality measure, and an even faster algorithm to compute the Shapley value-based betweenness centrality measure, both for weighted and unweighted networks. Interestingly, for the unweighted case, our algorithm for computing the Shapley value-based centrality has the same complexity as the best known algorithm for computing the standard betweenness centrality due to Brandes 15. We empirically evaluate our measures in a simulated scenario where nodes fail simultaneously. We show that, compared to the standard measure, the ranking obtained by our measures reflects more accurately the influence that different nodes have on the functionality of the network. We propose a betweenness centrality based on the Shapley value and Semivalue.We develop polynomial algorithms for our game-theoretic metrics.We evaluate our measures in scenarios where simultaneous node failures occur.Our measures obtain better results than standard betweenness centrality.We provide an empirical evaluation of algorithms on real-life and random graphs.

Efficient Computation of Semivalues for Game-Theoretic Network Centrality

Solution concepts from cooperative game theory, such as the Shapley value or the Banzhaf index, have recently been advocated as interesting extensions of standard measures of node centrality in networks. While this direction of research is promising, the computation of game-theoretic centrality can be challenging. In an attempt to address the computational issues of game-theoretic network centrality, we present a generic framework for constructing game-theoretic network centralities. We prove that all extensions that can be expressed in this framework are computable in polynomial time. Using our framework, we present the first game-theoretic extensions of weighted and normalized degree centralities, impact factor centrality, distance-scaled and normalized betweenness centrality, and closeness and normalized closeness centralities.

A new approach to measure social capital using game-theoretic techniques

Although the notion of social capital has been extensively studied in various bodies of the literature, there is no universally accepted definition or measure of this concept. In this article, we discuss a new approach for measuring social capital which builds upon cooperative game theory. The new approach not only turns out to be a natural tool for modeling social capital, but also captures various aspects of this phenomenon that are not captured by other approaches.