LIDDE: A differential evolution algorithm based on local-influence-descending search strategy for influence maximization in social networks

Qiu, Liqing; Tian, Xiangbo; Zhang, Jianyi; Gu, Chunmei; Sai, Shiqi

doi:10.1016/j.jnca.2020.102973

Cited by 28 publications

(11 citation statements)

References 23 publications

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“…Meanwhile, only the undergraduates' political opinions are collected. In addition, three previous researches, that is, CAOM, 10 IMGA 11 and LIDDE 12 are used as the benchmarks. CAOM is a community‐based approach which includes community detection, selection of candidate nodes and generation of seed nodes.…”

Section: Methodsmentioning

confidence: 99%

Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking

Liu

2021

Internet Technology Letters

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Social Information‐Centric Networking (SICN) is an integration of Social Network (SN) and ICN, which is a popular paradigm as most of contents in SN depend on the transmission and interaction of ICN. In SICN, the research on opinion formation maximization is very important. Therefore, this paper considers the scenario regarding the political education of undergraduates and proposes a heuristic opinion formation maximization algorithm, including two stages. (a) The opinion formation model is built based on individual intimacy and social influence. (b) In order to make sure the opinion formation maximization, the collaborative evolution model is built. By comparing with three latest researches, the proposed heuristic algorithm can save the required identification time by 0.379 hour, 0.687 hour and 0.889 hour respectively. The experimental results further demonstrate that the research on opinion formation maximization in SICN makes for the improvement on the political education of undergraduates.

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Section: Methodsmentioning

confidence: 99%

Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking

Liu

2021

Internet Technology Letters

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“…The population number parameter, the mutation weight parameter, and the C parameter are the probability of recombination or intersection, which is multiplied by the difference of the two vectors and added to the third vector. The F parameter is usually set between 0 and 2 and the C r parameter is between 0 and 1 [47]. In general, this algorithm has different stages, mutation, intersection or recombination, and finally the selection, which is described in Fig.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Nguyen¹,

Hai²,

Al‐Ansari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

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“…Tang et al [9] analyzed the effect of network topology characteristics, based on which a discrete shuffled frog-leaping algorithm was used for IM. Liu et al [34] used a local-influence-descending (LID) search strategy to construct a candidate set of nodes with relatively large influence, based on which a differential evolution (LIDDE) algorithm was developed for the IM problem. With sufficient iterations, LIDDE can perform with both accuracy and efficiency, but the results may be affected by network structures.…”

Section: Metaheuristic Approachesmentioning

confidence: 99%

“…Designing a metaheuristic algorithm for an NP-hard problem does not require an investigation of the properties of the problem, which saves considerable time and energy over problem-specific algorithms [37,31], such as the greedy-based CELF [19]. Evolutionary approaches have been reported to have good global search capability [30,34,31], and adaptive simulated annealing approaches can accelerate convergence [38,26,39,40]. Hence, we adopt these two approaches.…”

Section: Phased Hybrid Evaluation-enhanced Frameworkmentioning

confidence: 99%

“…Evolutionary algorithms often employ a crossover strategy to generate a mixed population based on the initial and mutated populations, to increase the probability of finding a global optimal solution. A common rule for generating a crossover individual is to select vertices from an initial individual and its corresponding mutated individual based on a predefined probability cp, a random number ran num , and the circumstance of the current vertex [34]. Specifically, let X and X m be the generated initial population and mutated population, respectively, of size pop.…”

Section: Randrde Crossovermentioning

confidence: 99%

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PHEE: A phased hybrid evaluation-enhanced approach for identifying influential users in social networks

Zhu¹,

Wang²,

Zhang³

et al. 2022

Preprint

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For the purpose of maximizing the spread of influence caused by a certain small number k of nodes in a social network, we are asked to find a k-subset of nodes (i.e., a seed set) with the best capacity to influence the nodes not in it. This problem of influence maximization (IM) has wide application, belongs to subset problems, and is NP-hard. To solve it, we should theoretically examine all seed sets and evaluate their influence spreads, which is time-consuming. Therefore, metaheuristic strategies are generally employed to gain a good seed set within a reasonable time. We observe that many algorithms for the IM problem only adopt a uniform mechanism in the whole solution search process, which lacks a response measure when the algorithm becomes trapped in a local optimum. To address this issue, we propose a phased hybrid evaluation-enhanced (PHEE) approach for IM, which utilizes two distinct search strategies to enhance the search of optimal solutions: a randomized range division evolutionary (RandRDE) algorithm to improve the solution quality, and a fast convergence strategy. Our approach is evaluated on 10 real-world social networks of different sizes and types. Experimental results demonstrate that our algorithm is efficient and obtains the best influence spread for all the datasets compared with three state-of-the-art algorithms, outperforms the timeconsuming CELF algorithm on four datasets, and performs worse than CELF on only two networks.

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LIDDE: A differential evolution algorithm based on local-influence-descending search strategy for influence maximization in social networks

Cited by 28 publications

References 23 publications

Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking

Smart opinion formation maximization to improve political education of undergraduates in Social Information‐Centric Networking

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

PHEE: A phased hybrid evaluation-enhanced approach for identifying influential users in social networks

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