Cellular goore game with application to finding maximum clique in social networks

Abstract

The goore game (GG) is a model for collective decision making under uncertainty, which can be used as a tool for stochastic optimization of a discrete variable function. The GG has a fascinating property that can be resolved in a distributed manner with no intercommunication between the players. The game has found applications in many network applications, including sensor networks, quality-of-service routing, and social networks. In this paper, we introduce an extension of GG called cellular goore game (CGG) for the first time. The CGG is a network of GGs. In this network, each node (or subset of nodes in the network) plays the rule of referees, each of which participates in a GG with its neighboring players (voters) at any time. Like in GG, each player independently selects its optimal action between two available actions based on their gains and losses received from its adjacent referee. Players in CGG know nothing about how other players are playing or even how/why they are rewarded/penalized. The potential of the CGG is shown by providing an algorithm for finding a maximum clique in social networks. Our research provides the first-time study of the CGG for finding a maximum clique in graphs. The performance of the CGG-based algorithm for finding maximum clique is studied on the standard clique benchmark called DIMACS by several experiments. The obtained result shows that the CGG-based algorithm is superior to the existing algorithms in terms of finding maximum clique size and time.

The Popularity of Extension Workers in the Exchange of Information on Yard Utilization

“…It shows that 121 smaller social networks were formed on social networks to exchange information on yard utilization among agricultural extension workers in Yogyakarta. According to Khomami et al (2022), What is interesting about a clique is that it represents a community or small group within a social network that shares more intuitive and closer relationships. The cliques formed will significantly affect the flow of information exchange.…”

A parameter-independent algorithm of finding maximum clique with Seidel continuous-time quantum walks

“…To mitigate this issue, advanced search heuristics like tabu search, 9 simulated annealing, 10 and neural network-based algorithms 11 have been proposed to reduce the risk of falling into local optimality. Some strategies may help improve the heuristics, 12 , 13 in which the auxiliary parameters or functions are still necessary. Classical heuristic algorithms struggle with large graphs, facing challenges like exponential search space growth, local optima traps, and high resource demands.…”