Finding Influential Nodes in Multiplex Networks Using a Memetic Algorithm

A new technique for influence maximization on social networks using a moth-flame optimization algorithm

In our modern digital era, social networks have seamlessly integrated into the fabric of our daily lives. These digital platforms serve as vital channels for communication, exchanging information, and cultivating valuable connections. The propagation of information within these social networks has emerged as a central focus for numerous sectors, including politics, marketing, research, education, and finance. Diverse models have been employed to depict the dynamics of information dissemination across these networks. Nevertheless, the notion of influence holds profound significance for both businesses and individuals. Influence maximization, particularly within the context of social networks, has garnered considerable attention owing to its potential to reach and impact a broad audience. This intricate challenge is commonly referred to as the "influence maximization problem," a problem well-known for its NP-hard complexity. This paper proposes a cutting-edge technique that leverages the Moth-Flame Optimization Algorithm to enhance influence maximization. Influence maximization is an important issue in network analysis, which widely occurs in social networks. Influence can be seen as a cascading effect, where the actions of a few trigger a chain reaction, ultimately reaching a large portion of the network. Identifying these "influencers" is crucial for efficient resource allocation and information dissemination. One of the important issues in finding the maximum influence is choosing the best vertex among all the vertices in the graph. This research presents a new method to find the maximum influence in social networks based on the Moth-Flame Algorithm (MFA). The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to the influence maximization problem. The experimental outcomes clearly delineate that the employment of the MFA (Maximal First Activation) approach effectively diminishes the execution time required to approximate the maximum influence. The proposed technique improved the accuracy and excucation time by 3.140 % and 12.2 % compared to other methods.

A New Consensus Model Based on Trust Interactive Weights for Intuitionistic Group Decision Making in Social Networks

A promising feature for group decision making (GDM) lies in the study of the interaction between individuals. In conventional GDM research, experts are independent. This is reflected in the setting of preferences and weights. Nevertheless, each expert's role is played through communication, collaboration, and cooperation with other individuals. The interaction from others may affect the power of an expert as well as his/her opinion. Furthermore, it is noted that a link path with the highest degree of trust is the most efficient information transmission channel. Inspired by these findings, an optimal trust-induced consensus process is designed with the usage of intuitionistic fuzzy preference relation. The comprehensive weight of each expert is decomposed into two portions, namely: 1) the individual weights and 2) interactive weights. Three optimization models are constructed to achieve weight parameters under different decision situations, where the weight parameters are represented through a 2-order additive fuzzy measure and the Shapley value. To reflect the interaction, the Choquet integral is employed for aggregating opinions, and a novel distance measure is adopted for accomplishing a consensus index. An illustrative example and comparison are put in practice to show the effectiveness and improvements of the proposed method.

Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral Clustering

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing image data spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing image datasets.

Finding influential edges in multilayer networks: Perspective from multilayer diffusion model

With the popularization of social network analysis, information diffusion models have a wide range of applications, such as viral marketing, publishing predictions, and social recommendations. The emergence of multiplex social networks has greatly enriched our daily life; meanwhile, identifying influential edges remains a significant challenge. The key problem lies that the edges of the same nodes are heterogeneous at different layers of the network. To solve this problem, we first develop a general information diffusion model based on the adjacency tensor for the multiplex network and show that the n-mode singular value can control the level of information diffusion. Then, to explain the suppression of information diffusion through edge deletion, efficient edge eigenvector centrality is proposed to identify the influence of heterogeneous edges. The numerical results from synthetic networks and real-world multiplex networks show that the proposed strategy outperforms some existing edge centrality measures. We devise an experimental strategy to demonstrate that influential heterogeneous edges can be successfully identified by considering the network layer centrality, and the deletion of top edges can significantly reduce the diffusion range of information across multiplex networks.

A Hybrid Evolutionary Algorithm for the Clique Partitioning Problem

The clique partitioning problem (CPP) of an edge-weighted complete graph is to partition the vertex set V into k disjoint subsets such that the sum of the edge weights within all cliques induced by the subsets is as large as possible. The problem has a number of practical applications in areas, such as data mining, engineering, and bioinformatics, and is, however, computationally challenging. To solve this NP-hard problem, we propose the first evolutionary algorithm that combines a dedicated merge-divide crossover operator to generate offspring solutions and an effective simulated annealing-based local optimization procedure to find high-quality local optima. The extensive experiments on three sets of 94 benchmark instances (including two sets of 63 classical benchmark instances and one new set of 31 large benchmark) show a remarkable performance of the proposed approach compared to the state-of-the-art methods. We analyze the key algorithmic ingredients to shed light on their impacts on the performance of the algorithm. The algorithm and its available source code can benefit people working on practical problems related to CPP.

Efficient Minimum Cost Seed Selection With Theoretical Guarantees for Competitive Influence Maximization

Minimum cost seed selection for competitive influence maximization, which selects a set of key users (called seed set) to spread its influence widely into the network at a minimum cost in a competitive social network, is a key algorithmic problem in social influence analysis. Due to its application potential in multiple fields, such as market expansion, election campaigns, and cultural competition, numerous studies have been emerging recently. Despite these efforts, this problem has not been satisfactorily solved since not only finding a (nearly) optimal solution for cost minimization but also evaluating a seed set is computationally complex. Existing works either trade approximation guarantees for practical efficiency using heuristics, or vice versa due to costly Monte Carlo simulations. In this article, a competitive reverse influence estimation-based greedy (CRIEG) algorithm, which provides bounded approximation guarantees, but offers significantly improved empirical efficiency under the competitive independent cascade model, is proposed. The core of the algorithm is a novel estimation method that improves the efficiency by constructing representative sketches to avoid heavy repeated simulations without compromising its performance guarantees. The experimental results on eight real-world networks with up to 1.13 million users show that compared with state-of-the-art algorithms, our algorithm is the most efficient while keeping the best performance, and can be orders of magnitude faster.

Cost-Aware Robust Control of Signed Networks by Using a Memetic Algorithm

The robust controllability (RC) of a complex system tries to select a set of dominating entities for the functional control of this entire system without uncertain disturbances, and the research on RC will help to understand the system's underlying functions. In this article, we introduce the control cost in signed networks and present a cost-aware robust control (CRC) problem in this scenario. The aim of CRC is to minimize the cost to control a set of dominating nodes and transform a set of unbalanced links into balanced ones, such that the signed network can be robustly controlled without uncertain unbalanced factors (like nodes and links). To solve this problem, we first model CRC as a constrained combination optimization problem, and then present a memetic algorithm with some problem-specific knowledge (like the neighbors of nodes, the constraints of CRC, and the fast computation of the cost under each optimization) to solve this problem on signed networks. The extensive experiments on both real social and biological networks assess that our algorithm outperforms several state-of-the-art RC algorithms.