Conference Agenda

This study examines the survival and evolution of 443K bidirectional mention ties on Twitter by merging datasets collected before 2015 and in the first few months of the COVID-19 pandemic (February to June, 2020). We hypothesize that strong pre-existing ties, marked by frequent communication and shared identities, endure and tolerate cognitive and stance differences over time. Our findings show that surviving ties are stronger than average pre-2015 ties but exhibit greater cognitive distance in COVID-19 discussions, suggesting that strong ties can tolerate different and even opposing opinions on contentious topics. This challenges traditional models of social influence and homophily, which predict increased cognitive similarity within strong ties. The findings imply the potential for old ties to function as network bridges, reducing political divides by connecting dissimilar social groups.

Despite extensive research on the relationship between international research collaboration (IRC) and research performance in science and technology (S&T), existing research has mostly examined single or comparative case studies, relatively small samples composed of developed countries, and uni-directional relations between empirical indicators. Although large scale network studies of IRC are becoming more common, 1) drivers of IRC network formation and 2) effects of the IRC network on policy-relevant performance outputs tend to be analyzed separately. Large scale analysis of the reciprocal dynamic relationship between IRC and national performance has yet to be conducted.

This research tests network effects on performance and vice versa simultaneously using a longitudinal co-evolution model on three decades of global S&T network and performance data. We employ the stochastic actor oriented model (SAOM) framework, also known as Siena models, to analyze data on 166 countries from 1993 to 2022. Yearly IRC networks are constructed from Web of Science's XML database. Corresponding national S&T performance data is gathered from Elsevier's fractional field-weighted citation index (FWCI), which disentangles national from internationally attributed citation impact. The models also account for geographic distance, national wealth, population metrics, political governance, and endogenous network processes.

The preliminary results support the hypotheses with positive and significant estimates for both effects. However, geographic distance appears to play a critical role in the transmission of the social effect of performance on the IRC network. Indeed, not controlling for geographic distance renders this effect insignificant in the face of the endogenous network dynamic of preferential attachment. Further analysis will be conducted incorporating different sensitivity tests in addition to tests for disciplinary and temporal heterogeneity.

Results of applying an Incremental Fuzzy Grouping algorithm on several real-world social media data sets will be presented. Fuzzy Grouping is a method of extracting group structures in which individual actors can be in multiple groups with different weights, at each point in time. Results of the developed Incremental Fuzzy Grouping algorithm will be compared with conventional grouping algorithms such as Leiden Grouping, K-Means Grouping and Spectral Clustering, applied on the same social media data set using various temporal window sizes. The ability of the proposed method to efficiently extract fuzzy groups over time, even for large scale data sets will be demonstrated through testing on data sets of different scale. In addition, the approximate computational cost and how that computational cost scales with data set size will also be compared between the proposed algorithm and conventional algorithms.

Note, the proposed fuzzy grouping algorithm does NOT operate on time windows pulled from the data set. Instead, it operates incrementally on the time stamped social media data set, overcoming one of the major challenges in analyzing over-time social media data using the conventional grouping algorithms. In addition, the proposed fuzzy group over-time tracking algorithm can employ an incremental algorithm for determining the “best” number of groups at any point in time. An advantage of the proposed algorithm is that it does not require the human user to select the time aggregation window or the desired # of groups.

Temporal changes in relationships are common in social networks. Do network structures change too or remain stable? Studying temporal change and stability in networks is especially important when actors engage in cross-sector collaboration over time to address complex public problems. This study argues that actors maintain regularities in temporal network structures and explores social processes underlying such regularities. Data constitute cross-sector collaboration ties, gathered through interview, that were created by 125 rural communities in Nepal with organizations in 2007 and in 2014 to help meet community needs. Data for 2007 captured such ties regarding the planning of drinking water projects for funding from the Rural Water Supply and Sanitation Program aimed at improving access to potable drinking water. Sixty-six communities received one-year funding whereas fifty-nine communities did not. Data for 2014 constituted post-funding collaboration ties between all 125 communities and organizations.

Network descriptives of collaboration networks for 2007 and 2014 are compared first. Next, bipartite exponential random graph models are estimated to determine network structures and nodal attributes affecting forming collaboration ties. Network structures include a tendency for popular organizations to become more popular and for communities and organizations to be part of network closure. Community attributes considered are size, remoteness, and future collaboration. Organization attributes include operative level (village, district, and central) and organization type. The estimates involve collaboration networks of all communities and of funded and unfunded communities with organizations separately for 2007 and 2014. The preliminary results indicate the existence of changes in collaboration ties along with stability of popularity structure. This adds to our knowledge that actors combine both change and structural stability in cross-sector collaboration networks.

The dynamics of personal relationships remain largely unexplored due to the inherent difficulties of the longitudinal data collection process. In this work, we analyze a dataset tracking the temporal evolution of a network of personal relationships among 900 people over the course of four years. We search for evidence that the network is in equilibrium, meaning that all macroscopic properties remain constant, fluctuating around stable values, while the internal microscopic dynamics are active. We find that the probabilities governing the network dynamics are stationary over time and that the degree distributions, as well as edge and triangle abundances match the theoretical equilibrium distributions expected under these dynamics. Furthermore, we verify that the system satisfies the detailed balance condition, with only minor point deviations, confirming that it is indeed in equilibrium. Remarkably, this equilibrium persists despite a high turnover in network composition, suggesting that it is an inherent characteristic of human social interactions rather than a trait of the individuals themselves. We argue that this equilibrium may be a general feature of human social networks arising from the competition between different dynamical mechanisms and also from the cognitive and material resources management of individuals. From a practical perspective, the fact that networks are in equilibrium could simplify data collection processes, validate the use of cross-sectional data-based methods like Exponential Random Graph Models, and inform the design of interventions. Our findings advance the understanding of collective human behavior predictability and our ability to describe it using simple mathematical models.