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A genetic algorithm concerning diversity information for solving the robust influence maximization problem on networks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 4745-4759页

作者： Chen, Minghao Wang, Shuai Zhang, Jiazhong Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China

The influence maximization problem is one of the hot research topics in the field of complex networks in recent years. The so-called influence maximization problem is how to select the seed set that propagates the largest amount of information on a given network. In practical applications, networks are often exposed to complicated environments, and both link-specific and node-specific attacks can have a significant impact on the network's propagation performance. Several pilot studies have revealed the crux of the robust influence maximization problem, but the current work available is not comprehensive. On the one hand, existing studies only consider the case that the network structure is stable or under linkspecific attacks, and few researches have concentrated on the case when the network structure is under node-specific attacks. On the other hand, the current algorithm fails to combine the information of the search process well to solve the robust influence maximization problem. Aiming at these deficiencies, in this paper, a metric for evaluating the robust influence performance of seeds under node-specific attacks is developed. Guided by this, a genetic algorithm (GA) maintaining the principle of diversity concern (DC) to solve the Robust influence maximization (RIM) problem is designed, called DC-GARIM. DC-GA-RIM contains several problem-orientated operators and fully considers diverse information in the optimization process, which significantly improves the search ability of the algorithm. The effectiveness of DC-GA-RIM in solving the RIM problem is demonstrated on a variety of networks. The superiority of this algorithm over other approaches is shown.

关键词： Complex networks influence maximization problem robustness optimization genetic algorithm

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A Sample Reuse Strategy for Dynamic influence maximization problem 18th

A Sample Reuse Strategy for Dynamic Influence Maximization P...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Zhang, Shaofeng Liu, Shengcai Tang, Ke Southern Univ Sci & Technol Shenzhen 518055 Peoples R China ASTAR Ctr Frontier AI Res CFAR Singapore 138632 Singapore

ISBN: (纸本)9789819722747;9789819722754

Dynamic influence maximization problem (DIMP) aims to maintain a group of influential users within an evolving social network to maximize the influence scope at any given moment. A primary category of DIMP algorithms focuses on updating reverse reachable (RR) sets designed for static social network scenarios to accelerate the estimation of influence spread. The generation time of RR sets plays a crucial role in algorithm efficiency. However, their update approaches require sequential updates for each edge change, leading to considerable computational costs. In this paper, we propose a strategy for batch updating the changes in network edge weights to maintain RR sets efficiently. We retain those with a high probability by calculating the probability that previous RR sets can be regenerated at the current moment. This method can effectively avoid the computational cost of updating and sampling these RR sets. Besides, we propose a resampling strategy that generates high-probability RR sets to make the final distribution of RR sets approximate to the sampling probability distribution under the current social network. The experimental results indicate that our strategy is both scalable and efficient. On the one hand, compared to the previous update strategies, the running time of our approach is insensitive to the number of changes in network weight;on the other hand, for various RR set-based algorithms, our strategy can reduce the running time while maintaining the solution quality that is essentially consistent with the static algorithms.

关键词： influence maximization problem Dynamic Social Network Sample Reuse

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Solving the Multitasking Robust influence maximization problem on Networks using a Multi-factorial Evolutionary Algorithm

Solving the Multitasking Robust Influence Maximization Probl...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Chen, Minghao Wang, Shuai Cai, Shun Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China

ISBN: (纸本)9798400701207

Recently, influence maximization has become a key research area in complex networks, focusing on selecting optimal seed sets for propagation in the network. However, networks often face complex environments with node/link failures or external attacks, making robustness critical for seed nodes. This robust influence maximization problem is not comprehensively addressed by existing approaches, which fail to combine information or knowledge across multiple scenarios. To address this, this paper introduces multi-task optimization theory, designing an evolutionary algorithm, MFEANet, to consider diversity gene information and multiple optimization scenarios simultaneously. MFEANet achieves competitive performance in experimental results compared to existing methods.

关键词： Complex networks influence maximization problem robustness evolutionary algorithm multi-tasking optimization

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A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

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COMPUTING 2021年第11期103卷 2437-2477页

作者： Aghaee, Zahra Ghasemi, Mohammad Mahdi Beni, Hamid Ahmadi Bouyer, Asgarali Fatemi, Afsaneh Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan Iran Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Shahid Madani Univ Dept Informat Technol & Commun Azarbaijan Tabriz Iran

The different communications of users in social networks play a key role in effect to each other. The effect is important when they can achieve their goals through different communications. Studying the effect of specific users on other users has been modeled on the influence maximization problem on social networks. To solve this problem, different algorithms have been proposed that each of which has attempted to improve the influence spread and running time than other algorithms. Due to the lack of a review of the meta-heuristic algorithms for the influence maximization problem so far, in this paper, we first perform a comprehensive categorize of the presented algorithms for this problem. Then according to the efficient results and significant progress of the meta-heuristic algorithms over the last few years, we describe the comparison, advantages, and disadvantages of these algorithms.

关键词： Social networks influence maximization problem Viral marketing influence spread Meta-heuristic algorithm

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A Heuristic Algorithm Focusing on the Rich-Club Phenomenon for the influence maximization problem in Social Networks 6

A Heuristic Algorithm Focusing on the Rich-Club Phenomenon f...

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6th International Conference on Web Research (ICWR)

作者： Aghaee, Zahra Beni, Hamid Ahmadi Kianian, Sahar Vahidipour, Mehdi Shahid Rajaee Teacher Training Univ SRTTU Fac Comp Engn Tehran Iran Azarbaijan Shahid Madani Univ Dept Informat Technol & Commun Tabriz Iran Univ Kashan Fac Elect & Comp Engn Dept Comp Kashan Iran

ISBN: (纸本)9781728110516

The strength of information diffusion on social networks depends on many factors, including the selected influential nodes. The problem of finding such nodes in the network is modeled by influence maximization problem, which faces two essential challenges: (1) inadequate selection of the seed nodes due to the lack of focus on the rich-club phenomenon and (2) high running time due to the lack of focus on pruning the graph nodes and localization. To solve these challenges, a computational localization-based RLIM algorithm is presented here to prevent the rich-club phenomenon. In this algorithm, the graph nodes are pruned based on the eigenvector centrality to reduce the computational overhead, and then the computations are performed locally using localization criteria. After that, influential nodes are selected by avoiding the rich-club phenomenon. In the RLIM algorithm, the seed nodes provided a better influence spread than the other algorithms. Experimental results on the synthetic and real-world datasets shows that the RLIM algorithm can verify the high effectiveness and efficiency than the comparable algorithms for an influence maximization problem.

关键词： influence maximization problem influence Spread Rich-Club Phenomenon Eigenvector Centrality

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An Effective Simulated Annealing for influence maximization problem of Online Social Networks

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Procedia Computer Science 2017年 113卷 478-483页

作者： Shi-Jui Liu Chi-Yuan Chen Chun-Wei Tsai Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan R.O.C. Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan R.O.C.

The influence maximization problem (IMP) is one of the most well-known problems in the research domain of online social networks (OSN) that has attracted the attention of many researchers from different disciplines in recent years. One of the reasons is that the speed of information propagation in the OSN can be increased if we can find out users that have maximum influence on other users. The traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although metaheuristic algorithms can be used to solve the IMP, there is still plenty of room for improvement. That is why an effective and efficient algorithm is presented in this paper. The proposed algorithm, called simulated annealing with search partition (SASP), is based on a search space partitioning mechanism to enhance the search performance of simulated annealing for the IMP. The experimental results show that the proposed algorithm outperforms the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations.

关键词： Metaheuristic algorithm simulated annealing influence maximization problem online social networks

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Efficient community-based influence maximization in large-scale social networks

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 44397-44424页

作者： Venunath, M. Sujatha, Pothula Koti, Prasad Dharavath, Srinu Pondicherry Univ Sch Engn & Technol Pondicherry India Sarada Gangadaran Coll Dept Comp Applicat Pondicherry India Annamalai Univ Dept Comp Sci & Engn Chidambaram 608002 India

The influence maximization problem (IMP) is a fundamental algorithmic challenge that involves selecting a set of k users, known as the seed set, from a social media network to maximize the expected number of influenced users or influence spread. In recent years, influence maximization has been extensively studied due to its numerous applications in marketing and other domains. However, many existing algorithms neglect the impact of communities on influence maximization, and some other methods suffer from scalability issues and time-consuming computations. In this paper, we propose a fast, and scalable algorithm called Community based Influential maximization (CB-IM) to address these limitations. This method leverages the community structure of the network to select k users, thus maximizing the influence spread. The CB-IM algorithm comprises two main components for influence maximization: (1) seed selection and (2) local community spreading. In the seed selection phase, we extract seed nodes from communities identified through a community detection algorithm. To effectively reduce computational complexity and distribute seed nodes across communities, we carefully select meaningful communities. The second phase involves the propagation of influence within independent communities. In this step, the final seed nodes are strategically distributed to facilitate local spreading through simple paths within the communities. To evaluate the performance of proposed method, we conducted a series of experiments using real networks. The proposed CB-IM algorithm demonstrated superior performance over other algorithms in terms of influence spread and running time, highlighting its effectiveness.

关键词： Complex networks influence maximization problem Community discovery Social networks

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A Memetic Algorithm for Solving the Robust influence problem on Directed Networks 14th

A Memetic Algorithm for Solving the Robust Influence Problem...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Ou, Zhaoxi Wang, Shuai Cai, Shun Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

The influence maximization problem has been intensively emphasized in recent studies, and the robust influence maximization problem is aimed at studying the information spreading ability of seeds under external attacks which is a current research hotspot. Elaborate diffusion models and seed determination methods are developed in previous studies. As a general form of networked systems, the plain undirected networks are thoroughly investigated. However, as indicated by related researches, directed networks show significance in daily life. Existing models and determination methods omit the effect of directness of networks, which may cause barriers in applications. Therefore, in order to study the influence robustness of directed networks, a metric, R-SD, is proposed to evaluate the robustness of influence pertaining to certain seeds in networks with directness. Then, a Memetic algorithm is devised to calculate the robust influence ability of seeds under intentional attacks, named MA-RIMD. The algorithm considers networks with directed links, and several problem-directed operators are maintained. Experimental results on several networks demonstrate that the algorithm exhibits a competitive convergence compared with existing approaches, and superior optimized results can be attained.

关键词： influence maximization problem robustness directed network Memetic algorithm

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IMC: influence maximization from Community View 23

IMC: Influence Maximization from Community View

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Proceedings of the 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Hongfang Pan Inner Mongolia Electric Power Digital Research Company Hohhot China China

ISBN: (纸本)9798400709203

influence maximization problem (IMP) has attracted much attention in recent years, which focuses on finding the most influential node set in social networks. Recently, community-based algorithm has been become a popular tactics. However, because the space of node selection is severely restricted with the size and location of the community, it leads to the important nodes are forced to be filter out in node selection stage, and then result the low quality of the selected nodes. Thus, we propose a novel community-based algorithm named IMC (influence maximization based on Community) in order to improve the diffusion effect in this paper. Experimental results on four real datasets show that the propagation effect of the proposed algorithm is better than other baseline algorithms no matter the size of dataset.

关键词： community discovery influence maximization problem seed node

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influence maximization in social networks with privacy protection

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年第0期607卷

作者： Zhang, Xian-Jie Wang, Jing Ma, Xiao-Jing Ma, Chuang Kan, Jia-Qian Zhang, Hai-Feng Anhui Univ Sch Math Sci Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Anhui Univ Sch Internet Hefei 230601 Peoples R China Anhui Sci & Technol Univ Sch Informat & Network Engn Bengbu 233030 Peoples R China

With the explosive development of online social network platforms, how to find a small subset of users (seed nodes) across multiple social networks to maximize the spread of information is of great significance. In reality, different platforms need to consider not only the commercial value of data, but also the protection of data privacy. In this situation, these multiple social platforms can be treated as a system of "multiple private social networks", which naturally arises a new problem: how to maximize the spread of information in multiple private social networks without breaking the protocol of privacy protection. In view of this, we propose an HE-IM algorithm to solve the problem from the perspective of cryptography. Specifically, we use the homomorphic encryption security protocol and the third-party servers to encrypt and decrypt the influence of nodes in each private network and update the set of seed nodes. The experimental results demonstrate that, by cooperatively fusing information from different private networks in a secret manner, our method can effectively find influential seed nodes to maximize influence in multiple private social networks. The performance of our method in maximizing influence range is much better than that of the baseline methods only considering the structure of single private network. Therefore, the method provides a new way for collaborative search of influential seed nodes in multiple private social networks.(c) 2022 Elsevier B.V. All rights reserved.

关键词： influence maximization problem Multiple private social networks Homomorphic encryption Privacy protection

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