Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization

Ма

IEEE Trans. Evol. Computat.

et al. 2021

Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy.Here we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. Experimental results on five real-world social networks, comparing our proposed method against several alternatives in current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

Ма

IEEE Trans. Evol. Computat.

et al. 2021

“…The identification of influential nodes is to pick a minimum of nodes as the initial seeds, which can achieve the maximum influenced scope, described as

where A is the initially infected nodes,

denotes the final influenced node set. This problem is simplified as top- k influencers identification by additional setting

, which has recently attracted great research interests [ 40 , 41 , 42 ]. A variety of real-world social networks are, in fact, interconnected by different types of interactions between nodes, forming what is known as multilayer networks.…”

Section: Modeling and Methodsmentioning

confidence: 99%

Identifying Influencers in Social Networks

Huang

Chen

et al. 2020

Entropy

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

“…The proposed algorithm was evaluated using a co-authorship data set and the obtained experimental results showed that the proposed algorithm outperforms two well-known benchmark heuristics. Other metaheuristic algorithm such as genetic algorithm [35], simulated annealing algorithm [36,37], particle swarm optimization algorithm [38,39] and cuckoo search algorithm [40] have been utilized for dealing with the influence maximization problem too. So, the researches in this Agha Mohammad Ali Kermani et al Comput Soc Netw (2021) 8:17 field have tried to develop approximation, heuristic or metaheuristic algorithms for finding the most influential nodes in social networks.…”

Section: Review Of the Literaturementioning

confidence: 99%

A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.