Biblio

Influence Maximization is an important research content in the dissemination process of information and behavior in social networks. Because Hill Climbing and Greedy Algorithm have good dissemination effect on this topic, researchers have used it to solve this NP problem for a long time. These algorithms only consider the number of active nodes in each round, ignoring the characteristic that the influence will be accumulated, so its effect is still far from the optimal solution. Also, the time complexity of these algorithms is considerable. Aiming at the problem of Influence Maximization, this paper improves the traditional Hill Climbing and Greedy Algorithm. We propose a Hybrid Distribution Value Accumulation Algorithm for Influence Maximization, which has better activation effect than Hill Climbing and Greedy Algorithm. In the first stage of the algorithm, the region is numerically accumulating rapidly and is easy to activate through value-greed. Experiments are conducted on two data sets: the voting situation on Wikipedia and the transmission situation of Gnutella node-to-node file sharing network. Experimental results verify the efficiency of our methods.

Metaheuristic search technique is one of the advance approach when compared with traditional heuristic search technique. To select one option among different alternatives is not hard to get but really hard is give assurance that being cost effective. This hard problem is solved by the meta-heuristic search technique with the help of fitness function. Fitness function is a crucial metrics or a measure which helps in deciding which solution is optimal to choose from available set of test sets. This paper discusses hill climbing, simulated annealing, tabu search, genetic algorithm and particle swarm optimization techniques in detail explaining with the help of the algorithm. If metaheuristic search techniques combine some of the security testing methods, it would result in better searching technique as well as secure too. This paper primarily focusses on the metaheuristic search techniques.