Explore the vast range of titles available.

At present, the existing influence evaluation algorithms often ignore network structure attributes, user interests and the time-varying propagation characteristics of influence. To address these issues, this work comprehensively discusses users’ own influence, weighted indicators, users’ interaction influence and the similarity between user interests and topics, thus proposing a dynamic user influence ranking algorithm called UWUSRank. First, we determine the user’s own basic influence based on their activity, authentication information and blog response. This improves the problem of poor objectivity of initial value on user influence evaluation when using PageRank to calculate user influence. Next, this paper mines users’ interaction influence by introducing the propagation network properties of Weibo (a Twitter-like service in China) information and scientifically quantifies the contribution value of followers’ influence to the users they follow according to different interaction influences, thereby solving the drawback of equal value transfer of followers’ influence. Additionally, we analyze the relevance of users’ personalized interest preferences and topic content and realize real-time monitoring of users’ influence at various time periods during the process of public opinion dissemination. Finally, we conduct experiments by extracting real Weibo topic data to verify the effectiveness of introducing each attribute of users’ own influence, interaction timeliness and interest similarity. Compared to TwitterRank, PageRank and FansRank, the results show that the UWUSRank algorithm improves the rationality of user ranking by 9.3%, 14.2%, and 16.7%, respectively, which proves the practicality of the UWUSRank algorithm. This approach can serve as a guide for research on user mining, information transmission methods, and public opinion tracking in social network-related areas.

Spike Neural Network (SNN) can provide an efficient reasoning process. In this paper, by modifying the Vovnet network to use IF neurons to replace the traditional Vovnet RELU neurons, testing on the open handwritten digit mnist data set, and comparing the Vovnet network structure of CNN and SNN for comparative experiments, it is found that the Spiking-Vovnet network is guaranteed and traditional Under the premise of the accuracy of the Vovnet network, the speed has been greatly improved, and at the same time, the resource consumption is reduced. In order to expand the application, in the synthetic aperture radar (SAR) image target detection, the Spiking-Vovnet network is used as the FCOS ship target detection feature extraction network. For promote the prise degree of detection, the variability convolutional neural network is lead into design a SAR ship Feature extraction network for ship target detection.

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network.

Mutualistic interactions among plants and animals have played a paramount role in shaping biodiversity. Yet the majority of studies on mutualistic interactions have involved only a few species, as opposed to broader mutual connections between communities of organisms.Mutualistic Networksis the first book to comprehensively explore this burgeoning field. Integrating different approaches, from the statistical description of network structures to the development of new analytical frameworks, Jordi Bascompte and Pedro Jordano describe the architecture of these mutualistic networks and show their importance for the robustness of biodiversity and the coevolutionary process. Making a case for why we should care about mutualisms and their complex networks, this book offers a new perspective on the study and synthesis of this growing area for ecologists and evolutionary biologists. It will serve as the standard reference for all future work on mutualistic interactions in biological communities.

Governance networks describe the complex relations among different types of actors involved in the governance of a policy issue. Here, we ask how different institutional and socioeconomic contextual conditions influence the structure of these networks and result in more horizontal or hierarchical types of governance networks. To answer this question, we study Vietnam's sanitation sector and compare two different provinces, Hanoi and Ben Tre. More specifically, we analyze networks of information exchange among key actors based on face-to-face interviews and prestructured questionnaires. We find that in the highly urbanized capital city of Hanoi, which serves as a national leader of innovation, where national and international actors are present, and where local actors have high capacities, information exchange tends to follow horizontal network structures. In the more rural, typical province of Ben Tre, hierarchical structures dominate.

Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

Using a multitier mapping of supply-chain relationships constructed from granular global, firm-to-firm supplier–customer linkages data, we quantify the degree of financial risk propagation from the supply network beyond firms’ direct supply-chain connections and isolate structural network properties serving as significant moderators of risk propagation. We first document a baseline fact: a significant proportion of tier-2 suppliers are shared by tier-1 suppliers. We then construct two simple metrics to capture the degree of tier-2 sharing and disentangle its effect from tier-2 suppliers’ own risks. We show that the focal firms’ risk levels are significantly related to the proportion of shared tier-2 suppliers in their supply network, and the effect becomes monotonically stronger as their tier-2 suppliers become more highly shared. Finally, we uncover causal relationships behind these associations using a new source of exogenous, idiosyncratic risk events in an event study setting. We show that, as tier-2 suppliers are impacted by these events, focal firms experience negative abnormal returns, the magnitude of which is significantly larger when the impacted tier-2 suppliers are more heavily shared. Overall, our study uncovers the subtier network structure as an important risk source for the focal firm, with the degree of tier-2 sharing as the main moderator. Our results also provide the microfoundation for a common structure in idiosyncratic risks and suggest the importance of incorporating the effect of subtier supply network structure in the portfolio-optimization process. This paper was accepted by Vishal Gaur, operations management.

The human social world is orders of magnitude smaller than our highly urbanized world might lead us to suppose. In addition, human social networks have a very distinct fractal structure similar to that observed in other primates. In part, this reflects a cognitive constraint, and in part a time constraint, on the capacity for interaction. Structured networks of this kind have a significant effect on the rates of transmission of both disease and information. Because the cognitive mechanism underpinning network structure is based on trust, internal and external threats that undermine trust or constrain interaction inevitably result in the fragmentation and restructuring of networks. In contexts where network sizes are smaller, this is likely to have significant impacts on psychological and physical health risks.

This study investigates the origins of variation in the structures of interorganizational networks across industries. We combine empirical analyses of existing interorganizational networks in six industries with an agent-based simulation model of network emergence. Using data on technology partnerships from 1983 to 1999 between firms in the automotive, biotechnology and pharmaceuticals, chemicals, microelectronics, new materials, and telecommunications industries, we find that differences in technological dynamism across industries and the concomitant demands for value creation engender variations in firms' collaborative behaviors. On average, firms in technologically dynamic industries pursue more-open ego networks, which fosters access to new and diverse resources that help sustain continuous innovation. In contrast, firms in technologically stable industries on average pursue more-closed ego networks, which fosters reliable collaboration and helps preserve existing resources. We show that because of the observed cross-industry differences in firms' collaborative behaviors, the emergent industry-wide networks take on distinct structural forms. Technologically stable industries feature clan networks, characterized by low network connectedness and rather strong community structures. Technologically dynamic industries feature community networks, characterized by high network connectedness and medium-to-strong community structures. Convention networks, which feature high network connectedness and weak community structures, were not evident among the empirical networks we examined. Taken together, our findings advance an environmental contingency theory of network formation, which proposes a close association between the characteristics of actors' environment and the processes of network formation among actors.

As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.