4.7 Article

A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks

Related references

Note: Only part of the references are listed.
Article Computer Science, Cybernetics

PIANO: Influence Maximization Meets Deep Reinforcement Learning

Hui Li et al.

Summary: This article presents a novel approach called PIANO, which leverages deep reinforcement learning to address the influence maximization problem. By incorporating network embedding and RL techniques, PIANO achieves superior performance compared to traditional solutions, as demonstrated through experimental studies on real-world networks.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2023)

Article Computer Science, Artificial Intelligence

Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

Lijia Ma et al.

Summary: This article proposes an evolutionary deep reinforcement learning algorithm called EDRL-IM for influence maximization in complex networks. By combining evolutionary algorithm and deep reinforcement learning algorithm, EDRL-IM outperforms state-of-the-art methods in finding seed nodes.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2023)

Article Computer Science, Artificial Intelligence

Evolutionary Multitasking for Feature Selection in High-Dimensional Classification via Particle Swarm Optimization

Ke Chen et al.

Summary: This article proposes a multitasking PSO approach for high-dimensional feature selection, which converts high-dimensional tasks into related low-dimensional tasks and transfers knowledge between them. By using feature importance, task generation strategy, and knowledge transfer mechanism, higher classification accuracy can be achieved.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

Article Computer Science, Artificial Intelligence

A Multivariation Multifactorial Evolutionary Algorithm for Large-Scale Multiobjective Optimization

Yinglan Feng et al.

Summary: The proposed multivariation multifactorial evolutionary algorithm aims to solve LSMOPs by conducting an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multivariation manner concurrently. This approach seamlessly transfers useful traits from simplified problem spaces to the original problem space, ensuring preservation of the original global optimal solution. Experimental results demonstrate the efficiency and effectiveness of the proposed method for large-scale multiobjective optimization compared to existing state-of-the-art methods.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

Article Computer Science, Artificial Intelligence

Solving Multitask Optimization Problems With Adaptive Knowledge Transfer via Anomaly Detection

Chao Wang et al.

Summary: MTEA-AD is a multitask evolutionary algorithm that learns inter-task relationships through anomaly detection models and transfers effective knowledge across tasks. It can adaptively adjust the degree of knowledge transfer and effectively reduce the risk of negative transfer through fair competition.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2022)

Article Automation & Control Systems

Evolutionary Multitasking Multilayer Network Reconstruction

Kai Wu et al.

Summary: This article introduces an evolutionary multitasking multilayer network reconstruction framework EM2MNR to enhance reconstruction performance by utilizing correlations among different component layers. By utilizing restricted Boltzmann machine to extract low effective features and deciding on knowledge transfer, the proposed framework significantly improves reconstruction performance on multilayer network reconstruction problems.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Computer Science, Artificial Intelligence

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

Lu Wang et al.

Summary: The study focuses on maximizing influence in social networks, proposing an influence assessment model based on both total valuation and variance in valuation of neighbor nodes, and developing a discrete moth-flame optimization method for searching influence-maximizing node sets. Experimental results show the effectiveness and robustness of the proposed method in tackling the influence maximization problem.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2021)

Article Computer Science, Artificial Intelligence

A Unified Framework of Graph-Based Evolutionary Multitasking Hyper-Heuristic

Xingxing Hao et al.

Summary: Hyper-heuristics and evolutionary multitasking share similarities in search methods, and by combining the advantages of both, the optimization of problems can be accelerated, leading to increased generality.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2021)

Article Automation & Control Systems

Finding Influential Nodes in Multiplex Networks Using a Memetic Algorithm

Shuai Wang et al.

Summary: This paper addresses the issue of finding influential nodes in realistic multiplex networks by designing an extended influence spreading model and developing a memetic algorithm. Experimental results validate the effectiveness of the algorithm, offering solutions for identifying potential propagators in multiplex social networks.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Computer Science, Artificial Intelligence

Identification of top-k influential nodes based on discrete crow search algorithm optimization for influence maximization

Huan Li et al.

Summary: In this study, a meta-heuristic discrete crow search algorithm (DCSA) is proposed to effectively solve the influence maximization problem. The algorithm utilizes a new coding mechanism, discrete evolution rules, degree-based initialization method, and random walk strategy to enhance search ability. Extensive experiments show that DCSA outperforms other algorithms in influence diffusion results under the independent cascade model.

APPLIED INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks

Lihong Han et al.

Summary: Identifying the top-k influential nodes remains a challenging research topic, and recent studies have shown that algorithms based on swarm intelligence can offer optimal global solutions. The proposed clique-DBA algorithm, based on clique partitioning, achieves stable and superior performance in estimating local influence values.

SOFT COMPUTING (2021)

Article Computer Science, Artificial Intelligence

Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm

Chao Wang et al.

Summary: IBMTEA-FCM is a random inactivation-based batch many-task evolutionary algorithm proposed to learn large-scale FCMs. By modeling the FCM learning problem as a many-task optimization problem, separating tasks into batches, and employing a many-task framework, IBMTEA-FCM achieves higher accuracy and lower computational cost in learning large-scale FCMs compared to existing classical methods.

APPLIED SOFT COMPUTING (2021)

Article Automation & Control Systems

Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem

Liang Feng et al.

Summary: This article introduces the concept of Evolutionary Multitasking (EMT), discusses the limitations of autoencoding-based EMT algorithm and explores the application of explicit EMT in combinatorial optimization problems. A new algorithm is proposed for solving the vehicle routing problem, and its effectiveness is validated through empirical studies.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Computer Science, Artificial Intelligence

A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks

Jianxin Tang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Evolutionary multitasking fuzzy cognitive map learning

Fang Shen et al.

KNOWLEDGE-BASED SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

ACO-IM: maximizing influence in social networks using ant colony optimization

Shashank Sheshar Singh et al.

SOFT COMPUTING (2020)

Article Computer Science, Artificial Intelligence

Identification of influential users in social network using gray wolf optimization algorithm

Ahmad Zareie et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II

Kavitesh Kumar Bali et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2020)

Article Computer Science, Artificial Intelligence

A survey on influence maximization in a social network

Suman Banerjee et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2020)

Article Business

Navigating the New Era of Influencer Marketing: How to be Successful on Instagram, TikTok, & Co.

Michael Haenlein et al.

CALIFORNIA MANAGEMENT REVIEW (2020)

Article Computer Science, Artificial Intelligence

Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Jinliang Ding et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Article Automation & Control Systems

Evolutionary Multitasking via Explicit Autoencoding

Liang Feng et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Article Computer Science, Artificial Intelligence

LAPSO-IM: A learning-based influence maximization approach for social networks

Shashank Sheshar Singh et al.

APPLIED SOFT COMPUTING (2019)

Article Computer Science, Artificial Intelligence

Evolutionary Multitasking Sparse Reconstruction: Framework and Case Study

Hao Li et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Article Computer Science, Artificial Intelligence

Back to the Roots: Multi-X Evolutionary Computation

Abhishek Gupta et al.

COGNITIVE COMPUTATION (2019)

Article Computer Science, Artificial Intelligence

Influence Maximization on Social Graphs: A Survey

Yuchen Li et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Round Influence Maximization

Lichao Sun et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Computer Science, Artificial Intelligence

Insights on Transfer Optimization: Because Experience is the Best Teacher

Abhishek Gupta et al.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2018)

Article Computer Science, Artificial Intelligence

An Efficient Memetic Algorithm for Influence Maximization in Social Networks

Maoguo Gong et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2016)

Article Computer Science, Artificial Intelligence

Multifactorial Evolution: Toward Evolutionary Multitasking

Abhishek Gupta et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2016)

Article Computer Science, Information Systems

Influence maximization in social networks based on discrete particle swarm optimization

Maoguo Gong et al.

INFORMATION SCIENCES (2016)

Article Multidisciplinary Sciences

Maximizing the Spread of Influence via Generalized Degree Discount

Xiaojie Wang et al.

PLOS ONE (2016)

Article Computer Science, Artificial Intelligence

A fast algorithm for finding most influential people based on the linear threshold model

Khadije Rahimkhani et al.

EXPERT SYSTEMS WITH APPLICATIONS (2015)

Article Computer Science, Information Systems

Real-time Targeted Influence Maximization for Online Advertisements

Yuchen Li et al.

PROCEEDINGS OF THE VLDB ENDOWMENT (2015)

Article Computer Science, Artificial Intelligence

Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond

Bo Liu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)

Article Computer Science, Information Systems

Sentic Computing for social media marketing

Erik Cambria et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2012)

Article Multidisciplinary Sciences

A 61-million-person experiment in social influence and political mobilization

Robert M. Bond et al.

NATURE (2012)

Article Computer Science, Artificial Intelligence

On the convergence of a class of estimation of distribution algorithms

QF Zhang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2004)

Article Physics, Multidisciplinary

Efficient immunization strategies for computer networks and populations

R Cohen et al.

PHYSICAL REVIEW LETTERS (2003)