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Coronavirus disease 2019 (COVID-19) was first discovered in China; within several months, it spread worldwide and became a pandemic. Although the virus has spread throughout the globe, its effects have differed. The pandemic diffusion network dynamics (PDND) approach was proposed to better understand the spreading behavior of COVID-19 in the US and Japan. We used daily confirmed cases of COVID-19 from 5 January 2020 to 31 July 2021, for all states (prefectures) of the US and Japan. By applying the pandemic diffusion network dynamics (PDND) approach to COVID-19 time series data, we developed diffusion graphs for the US and Japan. In these graphs, nodes represent states and prefectures (regions), and edges represent connections between regions based on the synchrony of COVID-19 time series data. To compare the pandemic spreading dynamics in the US and Japan, we used graph theory metrics, which targeted the characterization of COVID-19 bedhavior that could not be explained through linear methods. These metrics included path length, global and local efficiency, clustering coefficient, assortativity, modularity, network density, and degree centrality. Application of the proposed approach resulted in the discovery of mostly minor differences between analyzed countries. In light of these findings, we focused on analyzing the reasons and defining research hypotheses that, upon addressing, could shed more light on the complex phenomena of COVID-19 virus spread and the proposed PDND methodology.

Parents of children with autism often experience a wide range of emotions in their daily lives. However, previous research has primarily focused on average levels of emotional challenges, neglecting the significance of daily emotion dynamics that may underlie parental psychological functioning. This study adopted a dynamic network approach to examine the strength of temporal connections within and between various emotions-referred to as emotion network density-and further explore its impacts on parental psychological distress. Participants included 76 Chinese parents (M = 36.36 years, SD = 3.95 years; 58 mothers) of children with autism. Parents reported their psychological distress at baseline and then completed measures of daily emotions over a 14-day period. The densities of overall, positive, and negative emotion networks were estimated using the Multilevel Vector Auto-Regression model. The results indicated that higher densities of the overall and negative emotion networks were associated with increased stress, anxiety, and depressive symptoms in parents. Further analysis of network components showed that the in-strength of fear and guilt (i.e., their likelihood of being affected by other emotions) and the out-strength of anger and guilt (i.e., their capacity to influence other emotions) were positively related to parental psychological distress. However, neither the overall density of the positive emotion network nor its specific components showed a significant relationship with parental psychological distress. These findings highlight the importance of considering the daily dynamics of emotions, particularly negative emotions, from a network perspective to better understand the development of psychological distress in parents of children with autism.

The long convergence time required to achieve high-precision position solutions with integer ambiguity resolution-enabled precise point positioning (PPP-RTK) is driven by the presence of ionospheric delays. When precise real-time ionospheric information is available and properly applied, it can strengthen the underlying model and substantially reduce the time required to achieve centimeter-level accuracy. In this study, we present and analyze the real-time PPP-RTK user performance using ionospheric corrections from multi-scale regional networks during a day with medium ionospheric disturbance. It is the goal of this contribution to measure the impact the network dimension has on the ambiguity-resolved user position through the predicted ionospheric corrections. The user-specific undifferenced ionospheric corrections are computed at the network side, along with the satellite phase biases needed for single-receiver ambiguity resolution, using the best linear unbiased predictor. Such corrections necessitate the parameterization of an estimable user receiver code bias, on which emphasis is given in this study. To this end, we process GPS dual-frequency data from four four-station evenly distributed CORS networks in the United States with varying station spacings in order to evaluate if and to what extent the ionospheric corrections from multi-scale networks can improve the user convergence times. Based on a large number of samples, our experimental results showed that sub-10 cm horizontal accuracy can be achieved almost instantaneously in the ionosphere-weighted partially-ambiguity-fixed kinematic PPP-RTK solutions based on corrections from a network with 68 km spacing. Most of the solutions (90%) were shown to require less than 6.0 min, compared to the ionosphere-float PPP solutions that needed 68.5 min. In case of sparser networks with 115, 174 and 237 km spacing, 50% of the horizontal positioning errors are shown to become less than one decimeter after 1.5, 4.0 and 7.0 min, respectively, while 90% of them require 10.5, 16.5 and 20.0 min. We also numerically demonstrated that the user’s convergence times bear a linear relationship with the network density and get shorter as the density increases, for both full and partial ambiguity resolution.

Prior research has extensively studied individual adoption and use of information systems, primarily using beliefs as predictors of behavioral intention to use a system that in turn predicts system use. We propose a model of acceptance with peer support (MAPS) that integrates prior individual-level research with social networks constructs. We argue that an individual's embeddedness in the social network of the organizational unit implementing a new information system can enhance our understanding of technology use. An individual's coworkers can be important sources of help in overcoming knowledge barriers constraining use of a complex system, and such interactions with others can determine an employee's ability to influence eventual system configuration and features. We incorporate network density (reflecting \"get-help\" ties for an employee) and network centrality (reflecting \"give-help\" ties for an employee), drawn from prior social network research, as key predictors of system use. Further, we conceptualize valued network density and valued network centrality, both of which take into account ties to those with relevant system-related information, knowledge, and resources, and employ them as additional predictors. We suggest that these constructs together are coping and influencing pathways by which they have an effect on system use. We conducted a 3-month long study of 87 employees in one business unit in an organization. The results confirmed our theory that social network constructs can significantly enhance our understanding of system use over and above predictors from prior individual-level adoption research.

This paper investigates the effect of network centrality and network density on the propensity to engage in positive and negative eWOM, using social networks usage as a moderating variable. The research method was Structural Equation Modeling, and the data were collected through a survey conducted on 436 respondents from Romania. Findings showed that centrality and density only affect negative eWOM intent, the relationship being stronger at higher levels of network usage. In consequence, influential network members are more readily inclined to produce unfavorable eWOM. Subsequently, companies should make continuous efforts to spot and turn around bad publicity online.

Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios.

The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node’s initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the “propinquity model,” in which we control and vary the distance of the second link established by a new node and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.

Network size, network density, and tie strength together determine the function of social network and affect the growth of entrepreneurial enterprises. However, how the role of network size, network density, and tie strength on the growth of entrepreneurial enterprises remains inconsistent, as well as the effect of organizational and personal network remains unclear. To solve these relationships, we employ meta-analysis to reach study goals by researching 31 independent samples from 28 references with 5,259 observations. Results have shown two main findings: (1) Both network size and tie strength have a positive and significant impact on the growth of entrepreneurial enterprises, while network density does not correlate with the growth. (2) Organizational network mainly plays a positive effect between network size and growth, while personal network plays a more significant role in the relationship of tie strength and growth than organizational network. These results promote managers to take productive strategies for entrepreneurial enterprises’ growth. Our study provides a meta-analysis to merge different sounds about the relationship of network properties to the growth of entrepreneurial enterprises, emphasizing moderators of organizational and personal networks among these above relationships. Thus, these findings make significant contributions to the field of entrepreneurship.

Purpose Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network. Design/methodology/approach Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network. Findings This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant. Research limitations/implications This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI. Originality/value First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.