دکتر حمیدرضا نویدی قاضیانی

In this paper, while taking into account the cooperative relationships between units, the problem of revenue allocation is considered as a coalitional game. In order for the allocation to be equitable, by relying on the concept of DEA efficiency, a new characteristic function is presented, and then using the concept of the Shapley value, which is a well-recognized concept in coalitional game theory, a unique solution is obtained for the revenue allocation problem. And finally, to evaluate the equitability of the performed revenue allocation, the Gini coefficient is utilized. A comparison of the Gini coefficient obtained for our method with those of some existing methods showed that our method is more equitable than the previous ones. This demonstrates how impactful the wise and accurate selection of the characteristic function is in the equitability of the results.

Given the importance of maximizing influence in a social network, studies in this field often seek to find the nodes that have the most influence on the social network if designated as primary seeds. In this study, to reduce the complexity of computation algorithms, the problem is divided into several groups that aim to find a group of influential people among users of a social network. In this paper, a framework is introduced for solving the problem of influence maximization, which is based on the member clustering by the K means method, to improve the classification of network users, the data are weighted and the problem is modeled and analyzed as an evolutionary game. Finally, calculate its evolutionary stable strategy. This framework has been tested on real social network data for Abrar University students and we have achieved results such as increasing classification accuracy, reducing error function and finding a stable strategy in the community.

In cooperative P2P networks, there are two kinds of illegal users, namely free riders and Sybils. Free riders are those who try to receive services without any sort of cost. Sybil users are rational peers which have multiple fake identities. There are some techniques to detect free riders and Sybil users which have previously been proposed by a number of researchers such as the Tit-for-tat and Sybil guard techniques. Although such previously proposed techniques were quite successful in detecting free riders and Sybils individually, there is no technique capable of detecting both these riders simultaneously. Therefore, the main objective of this research is to propose a single mechanism to detect both kinds of these illegal users based on Game theory. Obtaining new centrality and bandwidth contribution formulas with an incentive mechanism approach is the basic idea of the present research’s proposed solution. The result of this paper shows that as the life of the network passes, free riders are identified, and through detecting Sybil nodes, the number of services offered to them will be decreased.