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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

The study utilises unique survey data gathered from 12,427 party members. The dependent variable measures party members’ in-party commitment and is based on willingness to donate money, to contribute effort, the feeling of belonging in the party network and social trust in the party network.

In this article, we study how different extra-parliamentary online and offline activities are associated with in-party commitment amongst political party members from the six largest Finnish parties. We especially delve into the differences between members of the Finnish parties.

We found that extra-parliamentary political activity, including connective action through social media networks and collective action through civic organisations, is highly associated with members’ in-party commitment. Additionally, members of the newer identity parties more effectively utilised social media networks, whilst the traditional interest parties were still more linked to traditional forms of extra-parliamentary political action.

By employing the sociological network theory perspective, the study contributes to ongoing discussions surrounding the impact of social media on political participation amongst party members, both within and beyond the confines of political parties.

Based on the perspective of knowledge management capability, this paper aims to reveal the internal mechanism of the digital empowerment of mobile social platforms to improve the operational performance of Chinese traditional retail enterprises. Such improvements have crucial theoretical value and practical implications for Chinese traditional retail enterprises to achieve transformation and sustainable development.

This study applied the typical analysis method, selected China’s leading mobile social platform, WeChat, as a typical case, and observed and analyzed the public data of the traditional retail industry and social platforms and interviews with relevant enterprises. On this basis, this study used the inductive and deductive methods of qualitative research to conduct an in-depth analysis of the mechanism by which WeChat’s digital empowerment improves the operational performance of Chinese traditional retail enterprises. It also discussed the critical role and path knowledge management capabilities play in this mechanism.

This research demonstrated that mobile social platforms empower Chinese traditional retail enterprises to build diversified digital channels, enhance the knowledge acquisition capability of enterprises and thus improve their performance; empower Chinese traditional retail enterprises to build digital community networks, enhance the knowledge diffusion capability of enterprises and thus improve their performance; and empower Chinese traditional retail enterprises to integrate online and offline businesses, enhance the knowledge integration capability of enterprises and thus improve their performance.

This study clarifies the internal mechanism of how the digital empowerment of mobile social platforms can improve the performance of Chinese traditional retail enterprises. This mechanism implies that knowledge management capabilities (knowledge acquisition, diffusion and integration capability) are the underlying logic for Chinese traditional retail enterprises to achieve higher performance levels. This has important practical implications for managers of Chinese traditional retail enterprises to leverage the digital infrastructure of mobile social platforms to achieve the sustainable development of enterprises.

This study provides an in-depth analysis of how the traditional retail industry uses digital social platforms to improve operational performance from the perspective of knowledge management capabilities, which can further promote the theoretical research and practical development of digitalization and knowledge management. At the same time, this study explored the research on the operational performance of Chinese traditional retail enterprises from the perspective of knowledge management capabilities and expanded the research on knowledge management in related fields. The authors have initially sorted out the impact of knowledge management capabilities on the operational performance of Chinese traditional retail enterprises in the digital era. This will help better understand the role and function of knowledge management in strategic transformation and expand the application of knowledge management theory.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

This study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.

The authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.

The study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.

The study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.

In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.

The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.