How does network intermediary affect collaborative innovation?Evidence from Chinese listed companies

Purpose:This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks.Design/methodology/approach:This study employs a mixed-method approach,combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance.Using a patent dataset of Chinese industrial enterprises,the research constructs the collaboration networks and analyzes their structural positions,particularly focusing on their role as intermediaries,characterized by betweenness centrality.Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes.Findings:The study reveals that firms in intermediary positions enhance collaborative innovation performance,but this effect is nuanced.A key finding is that network clustering negatively moderates the intermediary-innovation relationship.Highly clustered networks,while fostering local collaboration,may limit the innovation potential of intermediaries.On the other hand,relationship strength,measured by collaboration intensity and trust among firms,positively moderates the intermediary-innovation link.Research limitations:This study has several limitations that present opportunities for further research.The reliance on quantitative social network analysis may overlook the complexity of intermediaries’roles,and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors.Additionally,cross-country comparisons are needed to assess the consistency of these dynamics in different contexts.Practical implications:The study offers practical insights for firms and policymakers.Organizations should strategically position themselves as network intermediaries to access diverse information and resources,thereby improving innovation performance.Building strong trust helps using network intermediary advantages.For firms in highly clustered networks,it is important to seek external partners to avoid limiting their exposure to new ideas and technologies.This research emphasizes the need to balance network diversity with relationship strength for sustained innovation.Originality/value:This research contributes to the literature by offering new insights into the role of network intermediaries,presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

Nonlinear Relationship and Its Evolutionary Trace between Node Degree and Average Path Length of China Aviation Network Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.

Nonlinear Relationship and Its Evolutionary Trace between Average Degree and Average Path Length of Edge Vertices of China Aviation Network Based on Complex Network

In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the average degree and the average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the average degree and average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Through regression analysis,it was found that the average degree had a logarithmic relationship with the average path length of edge vertices and the two parameters of the logarithmic relationship had linear evolutionary trace.

City Networks of Online Commodity Services in China:Empirical Analysis of Tmall Clothing and Electronic Retailers

City networks have been a critical topic in the fields of urban geography and regional economics. Numerous studies have explored city networks, focusing mainly on infrastructure and industrial networks. Unlike traditional urban network of which the major measuring indexes are population sizes and entity industries, online commodity service networks could reflect well the influencing of emerging economies, especially the Internet economy, on city networks. This study analyzes and reveals structural features of China's city networks through online commodity services, providing the internet economic approach on city networks. Results indicate that the core cities of online commodity service networks are mainly concentrated in eastern coastal areas. In addition, spatial polarization and layer structure of network connections are obvious, descending from the centers in eastern China to peripheral cities in central and western China. Online commodity services of different cities show apparent differences and uncertainties in terms of specialization rates of international connection, which presents a tendency toward diversification. Online commodity service networks are not only associated with goods production, supply, and consumption in physical space but also reflect virtual information, capital, and technology flows, thus providing a new empirical approach for understanding city networks in information and internet economic age.

An Estimation Method for Relationship Strength in Weighted Social Network Graphs

Previous works mainly focused on estimating direct relationship strength in social networks. If two users are not directly connected in a social network, there is no direct relationship. In order to estimate the relationship strength between two indirectly connected users as well as directly connected users, this paper proposes an estimation method for relationship strength in weighted social network graphs, which is based on the trust propagation strategy and the estimation of direct relationship strength. Our method considers the length of a relationship path, the number of relationship paths and the edge weights (direct relationship strength) along with a relationship path to estimate the strength of indirect relationship. Then it synthesizes the direct and indirect relationship strength to represent the strength of relationship between two users in social net- works. Thus our method can fully estimate the relationship strength between any two users in a social network no matter whether they are directly connected or not.

Analysis on Refinery System as a Complex Task-resource Network

Refinery system, a typical example of process systems, is presented as complex network in this paper. The topology of this system is described by task-resource network and modeled as directed and weighted graph, in which nodes represent various tasks and edges denote the resources exchanged among tasks. Using the properties of node degree distribution, strength distribution and other weighted quantities, we demonstrate the heterogeneity of the network and point out the relation between structural characters of vertices and the functionality of correspond- ing tasks. The above phenomena indicate that the design requirements and principles of production process contrib- ute to the heterogeneous features of the network. Besides, betweenness centrality of nodes can be used as an impor- tance indicator to provide additional information for decision making. The correlations between structure and weighted properties are investigated to further address the influence brought by production schemes in system con- nectivity patterns. Cascading failures model is employed to analyze the robustness of the network when targeted at- tack happens. Two capacity assignment strategies are compared in order to improve the robustness of the network at certain cost. The refinery system displays more reliable behavior when the protecting strategy considers heteroge- neous properties. This phenomenon further implies the structure-activity relationship of the refinery system and provides insightful suggestions for process system design. The results also indicate that robustness analysis is a _promising applicat!on of methodologies from complex networks to process system engineering..

Contrastive Analysis of Software Networks Based on Different Coupling Relationships

Several software network models are constructed based on the relationships between classes in the object-oriented software systems.Then,a variety of well-known open source software applications are statistically analyzed by using these models.The results show that: (1) Dependency network does play a key role in software architecture;(2) The exponents of in-degree and total-degree distribution functions of different networks differ slightly,while the exponent of out-degree varies obviously;(3) Weak-coupling relationships have greater impact on software architecture than strong-coupling relationships.Finally,a theoretically analysis on these statistical phenomena is proposed from the perspectives of software develop technology,develop process and developer’s habits,respectively.

Academic Collaborator Recommendation Based on Attributed Network Embedding

Purpose:Based on real-world academic data,this study aims to use network embedding technology to mining academic relationships,and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks.Design/methodology/approach:We propose an academic collaborator recommendation model based on attributed network embedding(ACR-ANE),which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes.The non-local neighbors for scholars are defined to capture strong relationships among scholars.A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space.Findings:1.The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors.2.It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously.Research limitations:The designed method works for static networks,without taking account of the network dynamics.Practical implications:The designed model is embedded in academic collaboration network structure and scholarly attributes,which can be used to help scholars recommend potential collaborators.Originality/value:Experiments on two real-world scholarly datasets,Aminer and APS,show that our proposed method performs better than other baselines.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Meteorological satellite stakeholder relationship network based on social network analysis

The meteorological satellite service range is extensive,and science and technology and related industries have become beneficiaries of it.The complex meteorological satellite stakeholder relationship warrants quantitative evaluation.This study investigates the meteorological satellite stakeholder relationship network to provide a new research perspective for meteorological satellites in the field of management.For literature analysis,16 meteorological satellite stakeholders are identified through keyword screening,classified,and coded.A meteorological satellite stakeholder relationship network model is then constructed through social network analysis(SNA).Ego,local,and overall networks are analyzed from three perspectives to measure the network principle and to form a relationship network coordination degree evaluation system.The improved analytic hierarchy process(AHP)-fuzzy comprehensive evaluation method is then used to determine index weights and evaluate the relationship network coordination process design comprehensively.In empirical analysis,data for the meteorological satellite Fengyun-4 are obtained through questionnaire survey and literature analysis.Ucinet6 is used to generate relationship networks and analyze various stakeholder roles and status,stakeholder relationship network coordination degree,and evaluation results.The results demonstrate that the competent meteorological satellite department,the meteorological administration,the National Meteorological Centre,and the government are in the center of the Fengyun-4 stakeholder relationship network,with coordination degree in an“average”state.Thus,establishing a stakeholder coordination mechanism may strengthen connection and promote the development of meteorological undertakings.

An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.

Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship （QSPR） parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab. Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellent alternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.

Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships

In this paper, the regular characteristic of -wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typical -wear particles spectrum is established according to the equipment structure , friction and wear rule and the characteristic of 'wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship ; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification.

The application of neural networks to comprehensive prediction by seismology prediction method

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

Network Biological Modeling:A Novel Approach to Interpret the Traditional Chinese Medicine Theory of Exterior-Interior Correlation Between the Lung and Large Intestine

Objective To study the common pathogenesis of pneumonia and colitis using modern biological network analysis tools,and to explore the theory that the lung and large intestine are exteriorly and interiorly related.Methods The relevant target genes(hereinafter,“targets”)of pneumonia and colitis were separately queried on the GeneCards database.The main targets of the two diseases were then screened out according to their correlation scores and intersected to obtain those common to the two diseases.Metascape was used to analyze the main and common targets identified,and the Database for Annotation,Visualization and Integrated Discovery(DAVID)was used to enrich and analyze the common targets.Cytoscape 3.7.2 software was used to build the network diagram.Results In total,54 targets,such as TNF,IL-10,IL-6,IL-2,IL-4,TLR4,TLR2,CXCL8,IL-17A and IFNG,etc.,are common to pneumonia and colitis,which are mainly enriched in these processes such as cytokine–cytokine receptor interaction,the Tcell receptor signaling pathway,the Toll-like receptor signaling pathway and the Jak-STAT signaling pathway.The Metascape modular analysis identified 11 modules for pneumonia,six modules for colitis,and two modules for the common targets.Conclusions Pneumonia and colitis have the same pathogenic targets and mechanisms of action and finally interact with each other through inflammatory reactions and immune responses.This provides a probable molecular mechanism that explains the theory that the lung and large intestine are exteriorly and interiorly related.

Prediction of Molar Absorptivities of Color Reagents and Their Color Reactions with Yttrium by Artificial Neural Networks

The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

Self-Organized Detection of Relationships in a Network

Multistate operations within a network result in high-dimensional, multivariate temporal data, and are useful for systems, which monitor access to network entities like resources, objects, etc. Efficient self organization of such multi-state network operations stored in databases with respect to relationships amongst users or between a user and a data object is an important and a challenging problem. In this work, a layer is proposed where discovered relationship patterns amongst users are classified as clusters. This information along with attributes of involved users is used to monitor and extract existing and growing relationships. The correlation is used to help generate alerts in advance due to internal user-object interactions or collaboration of internal as well as external entities. Using an experimental setup, the evolving relationships are monitored, and clustered in the database.