Finite-Level Quantized Consensus of Multi-agent Networked Systems

This paper is concerned with the analysis of consensus multi-agent networked system. Adopted in the analysis is the finite-level logarithmic quantizer, for the transmission of the networked-agents state. Two protocols are utilised in the analysed multi-agent networked system: the consensus protocol, which is determined from the outputs and states of a set of encoder-decoder pair that is employed in the system, and convergence rate protocol that is precisely characterized via the use of a dynamic scaling factor. With information exchange among neighbouring agents, the asymptotic consensus can be reached. The proof of protocols is based on proper selection of parameters of the logarithmic quantizer chosen for the connected network. As a demonstration of the validity of the protocols, a four-agent networked system is used. It is shown that an undirected network exchange of information via a communication channel that is equipped with a set of encoder and decoder can lead to attainment of estimates of neighbour state protocol for the networked system. Furthermore, desired asymptotic convergence can be reached through appropriate choice of parameters of the logarithmic quantizer.

1370Introducing network analysis to measure early life adversity

Focus of Presentation Many studies have investigated associations between early life adversity (ELA) and outcomes across the life course. A defining characteristic of ELA is its complex nature, as many individual adverse experiences (e.g., parental mental health problems, financial difficulties) co-occur and interact over time. Commonly used methods for measuring ELA have not been able to elucidate pathways through which individual AEs are associated with each other during early life. We propose using network analysis to overcome this research gap. Findings Figure 1 shows the conditional associations between AEs in childhood and adolescence in an undirected network model, based on empirical data from the longitudinal TRAILS cohort. First, we found that the network model allows us to explain co-occurrences between AEs. For example: the co-occurrence of parental illness and financial difficulties in childhood is likely due to parental unemployment. Second, we identified which AEs are associated over time, e.g., familial conflicts in childhood and adolescence are strongly associated, the latter being associated with parental divorce in adolescence. Conclusions/Implications These findings add to the literature by providing insight into how individual AEs are conditionally associated, in distinct developmental periods and over time. The findings can be used in future research on pathways between AEs and guide the development of interventions. Key messages Undirected network models are a promising alternative approach to measuring ELA that can provide insight into pathways through which AEs co-occur and interact over time.

Spectral Clustering of Psychological Networks

Spectral clustering is a well-known method for clustering the vertices of an undirected network. Although its use in network psychometrics has been limited, spectral clustering has a close relationship to the commonly-used walktrap algorithm. In this paper, we report results from four simulation experiments designed to evaluate the ability of spectral clustering and the walktrap algorithm to recover underlying cluster structure in networks. The salient findings include: (1) the cluster recovery performance of the walktrap algorithm can be improved slightly by using exact K-means clustering instead of hierarchical clustering; (2) K-means and K-median clustering led to comparable recovery performance when used to cluster vertices based on the eigenvectors of Laplacian matrices in spectral clustering; (3) spectral clustering using the unnormalized Laplacian matrix generally yielded inferior cluster recovery in comparison to the other methods; (4) when the correct number of clusters was provided for the methods, spectral clustering using the normalized Laplacian matrix led to better recovery than the walktrap algorithm; (5) when the number of clusters was unknown, spectral clustering using the normalized Laplacian matrix was appreciably better than the walktrap algorithm when the clusters were equally-sized, but the two methods were competitive when the clusters were not equally-sized. Overall, both the walktrap algorithm and spectral clustering of the normalized Laplacian matrix are effective for partitioning the vertices of undirected networks, with the latter performing better in most instances.

Assessment of the Quality of Governance and Competitiveness at the Local Level: The Case of Slovenian Municipalities

The article draws on research on the quality of governance and its impact on Slovenian municipalities’ competitiveness. It presents the research results guided by two hypotheses: “Municipalities with a higher quality of governance are more competitive” and “The assessments of the quality of governance in Slovenian municipalities vary more than the assessments of the quality of governance in EU regions”. Starting from the general idea of governance as an undirected network of vertical and horizontal relations supports the importance of the research on the quality of governance and public administration in the EU environment on the local, regional, and national levels. As the low quality of governance impacts divergence in cohesion, mainly less developed and catching-up EU countries should focus on the development of the quality of governance and competitiveness at the regional and local levels. According to the research results, the Slovenian municipalities form several groups according to the behavior measured by the correlation between the quality of governance and the level of competitiveness. Additionally, the international comparison indicates that the quality of governance measured in Slovenian municipalities varies at least as much as in the EU regions. The evidence supports the quest for further research on the quality of governance and competitiveness to understand the dynamics of the development at the local and regional levels and develop proposals for improvement and measures adapted to the needs and capacities of groups of municipalities with similar behavior.

On the Accelerated Convergence of the Decentralized Event-triggered Algorithm for Convex Optimization

This article considers a problem of solving the optimal solution of the sum of locally convex cost functions over an undirected network. Each local convex cost function in the network is accessed only by each unit. To be able to reduce the amount of computation and get the desired result in an accelerated way, we put forward a fresh accelerated decentralized event-triggered algorithm, named as A-DETA, for the optimization problem. A-DETA combines gradient tracking and two momentum accelerated terms, adopts nonuniform step-sizes and emphasizes that each unit interacts with neighboring units independently only at the sampling time triggered by the event. On the premise of assuming the smoothness and strong convexity of the cost function, it is proved that A-DETA can obtain the exact optimal solution linearly in the event of sufficiently small positive step-size and momentum coefficient. Moreover, an explicit linear convergence speed is definitely shown. Finally, extensive simulation example validates the usability of A-DETA.

Impact of a public policy restricting staff mobility between long-term care homes in Ontario, Canada during the COVID-19 pandemic

ObjectivesTo assess changes in the mobility of staff between long-term care homes in Ontario, Canada before and after enactment of public policy restricting staff from working at multiple homes.DesignPre-post observational study.Setting and Participants623 long-term cares homes in Ontario, Canada between March 2020 and June 2020.MethodsWe used anonymized mobile device location data to approximate connectivity between all 623 long-term care homes in Ontario during the 7 weeks before (March 1 – April 21) and after (April 22 – June 13) the policy restricting staff movement was implemented. We visualized connectivity between long-term care homes in Ontario using an undirected network and calculated the number of homes that had a connection with another long-term care home and the average number of connections per home in each period. We calculated the relative difference in these mobility metrics between the two time periods and compared within-home changes using McNemar’s test and the Wilcoxon rank-sum test.ResultsIn the period preceding restrictions, 266 (42.7%) long-term care homes had a connection with at least one other home, compared to 79 (12.7%) homes during the period after restrictions, a drop of 70.3% (p <0.001). The average number of connections in the before period was 3.90 compared to 0.77 in after period, a drop of 80.3% (p < 0.001). In both periods, mobility between long-term care homes was higher in homes located in larger communities, those with higher bed counts, and those part of a large chain.Conclusions and ImplicationsMobility between long-term care homes in Ontario fell sharply after an emergency order by the Ontario government limiting long-term care staff to a single home, though some mobility persisted. Reducing this residual mobility should be a focus of efforts to reduce risk within the long-term care sector during the COVID-19 pandemic.

Weighted trapping time of weighted directed treelike network

With the deepening of research on complex networks, many properties of complex networks are gradually studied, for example, the mean first-passage times, the average receive times and the trapping times. In this paper, we further study the average trapping time of the weighted directed treelike network constructed by an iterative way. Firstly, we introduce our model inspired by trade network, each edge [Formula: see text] in undirected network is replaced by two directed edges with weights [Formula: see text] and [Formula: see text]. Then, the trap located at central node, we calculate the weighted directed trapping time (WDTT) and the average weighted directed trapping time (AWDTT). Remarkably, the WDTT has different formulas for even generations and odd generations. Finally, we analyze different cases for weight factors of weighted directed treelike network.