Remaining Agile and Adaptable

If a network has longevity, it will experience change. This chapter is about how networks reinvent themselves, mature, learn, grow, and dissolve. It uses a framework based on two distinctions: the goal-directedness of the network and the disruptiveness of the change. For serendipitous networks, or networks where organizations do not share goals, field-wide disruption and the accumulation of individual organizations’ actions drive change. In these circumstances, organizations manage changes by attending to their network portfolio and absorptive capacity. For goal-directed networks, change can be planned or unplanned. It can be incremental or radical. In each of these circumstances, the chapter recommends pathways for managing the degree and type of change. It uses case studies to illustrate how leaders manage the dilemmas caused by network change. It includes strategic planning, action learning team, and absorptive capacity tools.

The impact of social network change and health decline: a qualitative study on experiences of older adults who are ageing in place

Background Older adults prefer to age in place. Social network change and health decline challenge ageing in place, as stressors that make age-related advantages disappear. The aim of this study was to explore social network change and health decline and its impact on older adults who are ageing in place. Method In-depth interviews (n = 16) were conducted with older adults who were ageing in place and who were experiencing health decline and social network change. Procedures for grounded theory building were followed to analyse the interviews with respondents who were discharged from the hospital less than 4 months ago (n = 7). Narrative analysis was conducted to reach a deeper understanding of the expected complexity of experiences of this targeted sample. Results Results encompass a typology with four types of impact: A. Sneak preview of old age, B. Disruptive transition into old age, C. Drastically ageing, and D. Steadily ageing. Additionally, indications were found that older adults should be able to move along the four types of impact and ideally could end up in quartile D, experiencing little or no impact at all (anymore). Conclusion The results present an optimistic view on the possibilities of older adults to continue ageing in place despite experiencing unavoidable and uncontrollable stressors in life. Also, the results provide leads for practice, to develop an action perspective for home care nurses and gerontological social workers to determine and reduce the impact of social network change and health decline on older adults who are ageing in place. Suggestions for further research would be to unravel how to detect temporal setbacks in successful ageing in place.

Habit formation viewed as structural change in the behavioral network

Habit formation is a process in which an action becomes involuntary. While goal-directed behavior is driven by its consequences, habits are elicited by a situation rather than its consequences. Existing theories have proposed that actions are controlled by these two distinct systems. Although canonical theories based on such distinctions are starting to be challenged, there is no theoretical framework that implements goal-directed behavior and habits within a single system. Here, we propose a novel theoretical framework by hypothesizing that behavior is a network composed of several responses. With this framework, we have shown that the transition of goal-directed actions to habits is caused by a change in a single network structure. Furthermore, we confirmed that the proposed network model behaves in a manner consistent with the existing experimental results reported in animal behavioral studies. Our results revealed that habit could be formed under the control of a single system rather than two distinct systems. By capturing habit formation as a single network change, this framework can help study habit formation for experimental and theoretical research.

Systems biology is a method for analyzing massive amounts of multidimensional data generated by omics technologies. Cross-validation of the various technological platforms is critical

The value of systems biology in cardiology is becoming more recognized. There has been a tremendous rise in the number of articles in the last two decades, as publicly available datasets have been provided online and high-throughput tissue analysis has become more prevalent. In animal models, however, the future of cardiovascular medicine is less likely to be reanalyzing data and more likely to be investigating the function of GWAS-identified SNPs or network change using informatics and gene-editing technologies. These techniques, when combined with other omics interrogations and rigorous experimental design, have the potential to improve our understanding of gene-to-disease pathways.Systems biology is a method for studying large amounts of multidimensional data generated by omics technologies and, more broadly, the transition to big data in health care.Cross-validation of the various technological platforms is critical because omics studies are prone to bias and overinterpretation.Investigators must carefully determine which publicly accessible datasets, if any, to employ while conducting a systems analysis. Despite the fact that network theory and machine learning may yield amazing outcomes, these methods are not yet standardized. The studies mentioned here are excellent examples, in part because they use empirical models to support emergent systems biology results. In the few successful cases, careful experimental design, including interventional research and clinical trials, is required, in addition to the insights supplied by bioinformatics analysis of omics approaches. While it may be tempting to use emergent qualities to capture these new discoveries in more fundamental concepts, we agree with the English philosopher William of Ockham when he says, "It is futile to do with more things what can be done with fewer."

Interorganizational Network Change

This chapter examines current theorizing and research on interorganizational network change, and considers its antecedents, processes, outcomes, and management. We perform a systematic review of this literature across several disciplines, including communication, management, organization studies, public administration, and technology studies. Combining the frameworks laid out by Kilduff and Tsai (2003) and Van de Ven and Poole (1995), we demonstrate that the process of goal-directed and serendipitous network change operates using different mechanisms. We highlight the dominant theories and research trends for both types of networks, then we conclude the chapter with a critique and offer four prescriptions for future research.

A social network analysis of the spread of COVID-19 in South Korea and policy implications

This study estimates the COVID-19 infection network from actual data and draws on implications for policy and research. Using contact tracing information of 3283 confirmed patients in Seoul metropolitan areas from January 20, 2020 to July 19, 2020, this study created an infection network and analyzed its structural characteristics. The main results are as follows: (i) out-degrees follow an extremely positively skewed distribution; (ii) removing the top nodes on the out-degree significantly decreases the size of the infection network, and (iii) the indicators that express the infectious power of the network change according to governmental measures. Efforts to collect network data and analyze network structures are urgently required for the efficiency of governmental responses to COVID-19. Implications for better use of a metric such as R0 to estimate infection spread are also discussed.

Recurrence in the evolution of air transport networks

Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time.