In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider using a prespecified decay function with a prespecified rate of decay. A problem then is that the chosen decay function could be misspecified yielding biased results and incorrect conclusions. In this paper, we introduce three parametric weight decay functions (exponential, linear, and one-step) that can be embedded in a relational event model. A statistical method is presented to decide which memory decay function and memory parameter best fit the observed sequence of events. We present simulation studies that show the presence of bias in the estimates of effects of the statistics whenever the decay, as well as the memory parameter, are not properly estimated, and the ability to test different memory models against each other using the Bayes factor. Finally, we apply the methodology to two empirical case studies.

Individuals filling specialized, interdependent organizational roles achieve coordinated task execution through effective communication channels. Such channels enable regular access to information, opportunities, and assistance that may enhance one’s understanding of the task environment. However, the time and effort devoted to maintaining those channels may detract from one’s duties by turning attention away from the task environment. Disrupted task environments increase information requirements, thus creating a dilemma in which individuals must sustain benefits offered by important communication channels and relieve burdens imposed by ineffective channels. Using separable temporal exponential random graph models (STERGMs), this paper examines the relationship between situational awareness (SA) and the propensity to sustain or dissolve preexisting communication channels during 10 disruptive events experienced sequentially by a large, multifaceted military organization during a 2-week training exercise. Results provide limited evidence that increased SA detracts from tie preservation; instead SA begins to predict tie preservation during the second week of the exercise. Patterns of organizational adaptation reveal that, over time, improvised coordinative roles increasingly fall upon those with elevated SA. These results suggest that over successive disruptions, the benefits of information provided by communication channels within interdependent, role-specialized organizations begin to outweigh the costs of sustaining those channels.

The National Institutes of Health’s Clinical and Translational Science Awards (CTSA) institutes have been created, in part, to have a positive impact on collaboration and team science. This study is the first to examine the associations between a CTSA hub, the Michigan Institute for Clinical and Health Research (MICHR), and investigators’ ego networks. We ran cross-sectional and panel models of the associations between consulting with MICHR and the ego network measure of two-step reach (TSR) – that is, colleagues of colleagues reachable in two steps – from a network of 2161 investigators who had co-submitted a grant proposal to an external sponsor in 2006. Our analyses covered the period 2004–2012, although some model specifications covered the shorter time period 2006–2010. Consulting with MICHR had positive associations with the size of and changes in an investigator’s TSR across and over time, even controlling for research productivity and organizational affiliation. For example, over the period 2006–2010 an investigator who consulted with MICHR reached 44 more individuals than a non-consulting investigator. This study expands our understanding of the indirect impacts that clinical and translational science institutes have on investigators’ scientific networks. This network-based approach might be useful in quantifying the impact of team science initiatives at the university level.

Research in the area of social networks and health has demonstrated that lay social network members play a critical role in the early stages of the illness career, influencing key decisions and pathways to formal care. Here, we revisit and extend this body of work, examining how the lay social network context can moderate the influence of treatment experiences on recovery outcomes as the illness career unfolds. To achieve this goal, we address two research questions, drawing on a longitudinal sample of people making their initial contact with the mental health treatment system: First, we explore how treatment experiences, lay social network characteristics, and recovery outcomes change over 2 years, beginning with the point of entry into treatment. Second, we examine whether the relationship between perceived treatment experiences and recovery outcomes is contingent on characteristics of the lay network context in which clients are socially embedded, focusing on the network's cultural orientation toward medical professionals. We find that positive treatment interactions facilitate improved self-esteem, mastery, role functioning, recovery optimism, and global functioning when the lay network culture is pro-medical, but largely have null effects on the recovery process when the lay network is more hostile to medical professionals.

Models of network evolution are based on the implicit assumption that network growth is continuous, uniform, and steady. Using the data collected from a large online-blogging platform, we show that the addition and removal of network ties by users do not occur sporadically at isolated nodes spread all over the network, as assumed by the vast majority of stochastic network models, but rather occur in brief bursts of intense local activity.

These bursts of network growth and attrition (addition and removal of network ties) are highly localized around focal nodes. Such network changes coincide with nearly instantaneous densification of the ties between the affected nodes, resulting in an increase of local clustering. Furthermore, we find that these network changes are tightly coupled to the dynamics of individual attributes, particularly the increase in homology between neighboring nodes (homophily) within the scope of the burst. Coincidence of the localized network change with the increase in homophily suggests a strong coupling between the selection and influence processes that lead to simultaneous elevation of assortativity and clustering.

We study the persistence of network segregation in networks characterized by the co-evolution of vertex attributes and link structures, in particular where individual vertices form linkages on the basis of similarity with other network vertices (homophily), and where vertex attributes diffuse across linkages, making connected vertices more similar over time (influence). A general mathematical model of these processes is used to examine the relative influence of homophily and influence in the maintenance and decay of network segregation in self-organizing networks. While prior work has shown that homophily is capable of producing strong network segregation when attributes are fixed, we show that adding even minute levels of influence is sufficient to overcome the tendency towards segregation even in the presence of relatively strong homophily processes. This result is proven mathematically for all large networks and illustrated through a series of computational simulations that account for additional network evolution processes. This research contributes to a better theoretical understanding of the conditions under which network segregation and related phenomenon—such as community structure—may emerge, which has implications for the design of interventions that may promote more efficient network structures.

Although the importance of hedgerows for sustainable agriculture and conservation of rural biodiversity is increasingly being recognized, obtaining insight into the spatial and temporal dynamics of hedgerow networks remains an important challenge for landscape ecologists, with the key factors driving changes in rural landscape structure especially deserving further attention. The present study analyses the long-term history of a hedgerow network landscape in Flanders, Belgium. A detailed reconstruction of the hedgerow network is made at five points in time, starting at the end of the 18th century until present, for 367 distinct 400 m×400 m samples. Whilst hedgerows were mainly concentrated around historical village centres and within valleys at the end of 18th century, the network expanded progressively during the 19th century. In the 20th century, the hedgerow network degraded strongly, with hedgerow density and connectivity declining and mesh-size heterogeneity and network fragmentation increasing, although the network recovered slightly during the 1990s. Different trajectories of change in hedgerow network structure were observed depending on landscape position, with both topography and village proximity significantly affecting hedgerow network dynamics. The present network structure was mainly governed by land use, with highly developed networks being predominantly associated with pasture. Three main conclusions arise from the results of this study. First, the role of land use and landscape position as basic factors steering hedgerow network dynamics at the landscape scale is demonstrated. Second, the long-term perspective of the study enabled insight into the poorly known expansion phase of hedgerow networks, linked mainly with the development of small-scale labour-intensive agriculture. Finally, the findings confirm the large-scale degradation of linear semi-natural habitats in European agricultural landscapes during most of the 20th century, and indicate that a pro-active rural policy can halt and even reverse this process.