Abstract
Influence maximization (IM) is fundamental to social network applications. It aims to find multiple seed nodes with an enormous impact cascade to maximize these nodes’ spread of influence in social networks. Traditional methods for solving influence maximization of the social network, such as the distance method, greedy method, and PageRank method, may suffer from issues of low calculation accuracy and high computational cost. In this paper, we propose a new bacterial foraging optimization algorithm to solve the IM problem based on the complete-three-layer-influence (CTLI) evaluation model. In this algorithm, a novel grid-based reproduction strategy and a direction-adjustment-based chemotaxis strategy are devised to enhance the algorithm’s searchability. Finally, we conduct comprehensive experiments on four social network cases to verify the effectiveness of the proposed algorithm. The experimental results show that our proposed algorithm effectively solves the social network’s influence maximization.
Highlights
With the rapid development of various new Internet technologies in recent years, many social network applications [1], such as Facebook, Twitter, WhatsApp, and Instagram [1, 2], have emerged and become increasingly fashionable
With an increasing number of users and interconnections, the social network applications usually serve as the popular information exchange platform where online nodes’ information and opinion are propagated in a word-of-mouth way [1], instead of the traditional communication channels, for example, landline and newspaper [2]. These social networks can serve as the virtual marketing platform for commercial advertisers [3], having the potential of saving costs and increasing profit. Under such an information exchange environment, the spread of various user information is dependent on the word-of-mouth communication coming from social circles because the word-of-mouth effect from a small group of seed nodes can lead to a broader range of cascading influences. erefore, selecting a set of seed nodes that maximize the spread of information in the network is a significant problem faced by social network decision-makers [4,5,6,7,8,9]. is problem is called the influence maximization (IM) problem
We propose a new bacterial foraging optimization algorithm (NDBFO) to solve the IM problem, formulated as a discrete optimization problem
Summary
With the rapid development of various new Internet technologies in recent years, many social network applications [1], such as Facebook, Twitter, WhatsApp, and Instagram [1, 2], have emerged and become increasingly fashionable. Some heuristic algorithms were improved and used to find a seed set of size k that can influence other seeds in a specific social network propagation mode. In this paper, based on the complete-three-layer-influence (CTLI) evaluation model proposed in our previous work [32], a new discrete grid-based BFO is proposed to deal with the complex IM problem of the social network. (1) Aiming at the complex CTLI problem, a new discrete grid-based bacterial foraging optimization algorithm (NDBFO) is proposed to approximate the propagation range of nodes in the IC model. In this algorithm, the reproduction operation is enhanced by an improved grid-based strategy. In the elimination-dispersal process, some bacteria are eliminated and a new replacement is initialized at random, which maintains the diversity of the population
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