Dynamic Social Network Analysis Research Articles

Dynamic social network analysis of a brief alcohol intervention trial in heavy-drinking college students shows spillover effects.

Heavy-drinking college students tend to have close social networks, and there is theoretical and empirical support for the idea that behavior change can spread through those networks via close ties. The objective of this research was to determine whether intervention-induced behavior change in a subset of heavy drinkers in a sociometric (whole) college class-year social network is transmitted to other heavy drinkers in the network, resulting in reduced behavioral risk and change in network ties. We conducted a controlled trial in which most of a first-year college class (N = 1236; 56.9% female) was enrolled, with alcohol use and social network ties measured early in each of three semesters. Following a baseline assessment, the network was divided into two groups, brief motivational intervention (BMI) and natural history control (NHC) according to dormitory residence location. A subset of heavy drinkers in each group was selected, and those in the BMI group received an in-person intervention. Using stochastic actor-oriented modeling, we found a significant tendency for participants in the BMI group to shed ties with individuals with similar drinking behaviors between the first and second semesters, relative to the NHC group. Furthermore, heavy drinkers with reciprocal ties to intervention recipients in the BMI group showed a significant reduction in drinks per week. Individual alcohol interventions appear to have effects both on behavior and network connections among individuals who did not receive the intervention. Continued research is needed to identify the optimal conditions for the diffusion of behavior change.

Modeling dynamic social networks using concept of neighborhood theory

Dynamic social network analysis basically deals with the study of how the nodes and edges and associations among them within the network alter with time, thereby forming a special category of social network. Geometrical analysis has been done on various occasions, but there is a difference in the approximate distances of nodes. Snapshots for social networks are taken at each time slot and then are bound for these studies. The paper will discuss an efficient way of modeling dynamic social networks with the concept of neighborhood theory of cellular automata. So far, no model that uses the concept of neighborhood has been proposed to the best of our knowledge and the literature survey. Besides cellular automata that has been important tool in various applications has remained unexplored in the area of modelling. To this extent the paper, is the 1st attempt in modelling the social network that is evolving in nature. A link prediction algorithm based on some basic graph theory concepts has also been additionally proposed for the emergence of new nodes within the network. Theoretical and programming simulations have been explained in support to the model. Finally, the paper will discuss the model with a real-life scenario.

Separating the wheat from the chaff: Bayesian regularization in dynamic social networks

In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an “event”, defined as a triplet of time, sender, and receiver of some social interaction. The key question that relational event models aim to answer is what drives the pattern of social interactions among actors. Researchers often consider a very large number of predictors in their studies (including exogenous effects, endogenous network effects, and interaction effects). However, employing an excessive number of effects may lead to overfitting and inflated Type-I error rates. Moreover, the fitted model can easily become overly complex and the implied social interaction behavior difficult to interpret. A potential solution to this problem is to apply Bayesian regularization using shrinkage priors to recognize which effects are truly nonzero (the “wheat”) and which effects can be considered as (largely) irrelevant (the “chaff”). In this paper, we propose Bayesian regularization methods for relational event models using four different priors for both an actor and a dyad relational event model: a flat prior model with no shrinkage, a ridge estimator with a normal prior, a Bayesian lasso with a Laplace prior, and a horseshoe prior. We apply these regularization methods in three different empirical applications. The results reveal that Bayesian regularization can be used to separate the wheat from the chaff in models with a large number of effects by yielding considerably fewer significant effects, resulting in a more parsimonious description of the social interaction behavior between actors in dynamic social networks, without sacrificing predictive performance.

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design.

Knowledge recombination for emerging technological innovations: The case of green shipping

The paper explores knowledge recombination by analysing how knowledge networks in established technological fields influenced the formation of the emerging field of green shipping in the period 2007–2018. Previous research has demonstrated that embeddedness, proximity, and status are important mechanisms for the evolution of single technological fields. We investigate if these mechanisms also apply across technological fields. By employing dynamic social network analysis models, we find that actors transferred knowledge across technological fields through (re)combination mechanisms, which affected the emergence of the new technological field, but in different ways. While embeddedness and proximity played an important role, status was less important.

Who has set whose agenda on social media? A dynamic social network analysis of Tweets on Paris attack

ABSTRACT This study investigates the agenda-setting theory in the context of social media through dynamic social network analyses of 102,145 Tweets in a week after Paris attack on Twitter. Results indicate that professional mass media organizations still hold a greater agenda-setting power than individual opinion leaders for setting the public agenda, as they obtain significantly more tweets, mentions, and replies from the public. While the overall media agenda significantly correlates with the agenda set by the individual opinion leaders on Paris attack, time-series analysis reveals the intermedia agenda-setting effects between mass media and individual opinion leaders on Twitter are immediate and decrease as time elapses. Methodological and theoretical implications are discussed.

ACSIMCD: A 2-phase framework for detecting meaningful communities in dynamic social networks

Detecting and analyzing community structure is a challenging topic in dynamic social network analysis. Although the number of methods in this area is on the rise, there are only a few algorithms that can discover meaningful communities based on different aspects of social networks. Indeed, social networks contain various information sources that can be used to analyze them. The most important part of this information is related to users’ topics of interest (content information) and users’ interactions (structure information). One promising solution to discover meaningful communities is to combine these two concepts. Based on this, we introduce ACSIMCD, a 2-phase framework for discovering and updating community structure without recomputing them from scratch at each snapshot. This article mainly includes two parts. In the first part, a static community detection algorithm which is called Content and Structure Information based Method for Community Detection (CSIMCD for short) is proposed to discover the initial community structure. The CSIMCD uses a hybrid approach founded on statistical and semantic measures to extract the users’ topics of interest. Accordingly, the original network is divided into several clusters (topical clusters) so that each one represents a distinct topic, then by performing a link analysis on each topical cluster, the communities are detected. In the second part, we propose ACSIMCD (Adaptive CSIMCD), an adaptive method for detecting and updating community structure in dynamic social networks. More precisely, the ACSIMCD explores the topics of interest of each changed node to identify the topical cluster it belongs to. After that, we update the community structure in this topical cluster, and we keep others as they are. We compare the ACSIMCD model with algorithms from different approaches including content-based methods on real-world networks. The experimental results showed that ACSIMCD produces a community structure of high quality from the perspective of links and interests compared with the classical methods, and that it is able to process network changes effectively in a reasonable time scale.

Mapping the Changing Structure of Conflict Networks in North and West Africa

ABSTRACT Contemporary conflict in North and West Africa is characterized by a high degree of social and political complexity. Hundreds of rebel groups and extremist organizations are involved in a shifting series of alliances and rivalries with regional governments and with each other. These changing relationships can be represented as a social network that provides both opportunities and constraints to violent organizations. To better address this complexity, this article models the temporal evolution of both opposition and cooperation networks using detailed information on nearly 40,000 events in North and West Africa from 1997–2020. Using a relational approach called Dynamic Social Network Analysis (DSNA), the article suggests that the increasing number of belligerents, increasing density of conflictual relationships, and polarization on powerful organizations capable of conducting extensive military operations make a peaceful resolution of the North and West African conflicts more elusive than ever.

Knowledge exchange and innovation in clusters: a dynamic social network analysis

ABSTRACT Innovation is deeply rooted in clusters and is strongly related to knowledge exchanges. In literature, scholars have started suggesting that innovation rates are expected to be higher in dynamic networks, where there is variation in roles and knowledge exchanges. This paper contributes to this debate by studying the correlation between knowledge exchange and innovation at the cluster level, with a dynamic view. Using dynamic social network analysis as a methodological approach and the literature on Collaborative Innovation Networks (COINs) and knowledge exchanges as the theoretical framework, we investigated how varying over time cluster members’ leadership role and contribution in the flow of knowledge benefit cluster-level innovation. We relied on data collected from a collaborative cluster-based platform, focusing on technological knowledge exchanges. Our findings indicate that there is a strong positive correlation between rotating leadership and innovation as well as between rotating contribution and innovation.

Applying a complex systems perspective to alcohol consumption and the prevention of alcohol-related harms in the 21st century: a scoping review.

A complex systems perspective has been advocated to explore multi-faceted factors influencing public health issues, including alcohol consumption and associated harms. This scoping review aimed to identify studies that applied a complex systems perspective to alcohol consumption and the prevention of alcohol-related harms in order to summarize their characteristics and identify evidence gaps. Studies published between January 2000 and September 2020 in English were located by searching for terms synonymous with 'complex systems' and 'alcohol' in the Scopus, MEDLINE, Web of Science and Embase databases, and through handsearching and reference screening of included studies. Data were extracted on each study's aim, country, population, alcohol topic, system levels, funding, theory, methods, data sources, time-frames, system modifications and type of findings produced. Eighty-seven individual studies and three systematic reviews were identified, the majority of which were conducted in the United States or Australia in the general population, university students or adolescents. Studies explored types and patterns of consumption behaviour and the local environments in which alcohol is consumed. Most studies focused on individual and local interactions and influences, with fewer examples exploring the relationships between these and regional, national and international subsystems. The body of literature is methodologically diverse and includes theory-led approaches, dynamic simulation models and social network analyses. The systematic reviews focused on primary network studies. The use of a complex systems perspective has provided a variety of ways of conceptualizing and analyzing alcohol use and harm prevention efforts, but its focus ultimately has remained on predominantly individual- and/or local-level systems. A complex systems perspective represents an opportunity to address this gap by also considering the vertical dimensions that constrain, shape and influence alcohol consumption and related harms, but the literature to date has not fully captured this potential.

Co-Orientation of Media Use: Studying Selection and Influence Processes in Social Networks to Link Micro Behavior of TV and YouTube Use to Meso-Level Structures

ABSTRACT Social interactions regarding media content are omnipresent and highlight that media use is closely linked to social life. However, surprisingly little is known about the related social dynamics and their consequences for media use and social relations. This study addresses this topic based on co-orientation theory and investigates the social dynamics of TV and YouTube use among adolescents. A three-wave panel survey among 336 pupils enables us to disentangle which effect the friendship network has on individual media use (RQ1) and which effect media use has on the development and maintenance of friendship ties (RQ2). Based on a multi-level approach of dynamic social network analysis and applying stochastic actor-oriented modeling, we find empirical evidence for processes of social influence regarding TV programs and YouTube channels. No support is found for the hypothesized social selection processes. Along with these empirical insights, the research design demonstrates how the micro-level of individual behavior can be linked with the meso-level of social groups. Furthermore, the insights can be used as a basis to understand larger-scale phenomena emerging on the macro-level of societies and media markets such as audience fragmentation and long-tail audience distributions across media contents.

Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

SummaryWe propose in this work a novel approach for dynamic social network analysis by combining an agent‐based model, an author‐topic model, and pretopology. We first introduce an analytical model for a dynamic social network associated with textual content using agent‐based and author‐topic models, namely, Textual‐ABM. The purpose of Textual‐ABM is to support for the concept exploitation of the “dynamics” of a social network, which contains not only network's structure transformation but also agent's interest variation over time. Agent's interest is revealed through topic probability distribution, which is estimated based on textual data using an author‐topic model. In addition to demonstrating the fluctuation of the social network related to textual content, we also exploit information propagation phenomena by proposing two expanded spreading models. The first model is an expanded model of an independent cascade model in which probability of infection is formed on homophily, namely H‐IC. We have implemented experiments on a collected dataset from the Neural Information Processing Systems Conference and have acquired satisfying results. Furthermore, we propose an extended model of pretopological cascade model from our previous work, namely, Textual‐PCM. The advantage of PCM comparison with classical cascade model is to utilize pseudoclosure function built from pretopology to define the more complex set of neighborhoods. In this work, we expand PCM to apply detail for a social network related to textual information. A toy example with some experiments and discussion is illustrated for Textual‐PCM. The work in this paper is an extended version of our paper dynamic social network analysis using author‐topic model presented in I4CS 2018 Conference.

Dynamic social network analysis and performance evaluation

Social media in today's age is on a tremendous rise in terms of its usage and the enormous amount of data it generates which includes personal details of users, their images and the content that is being shared on various open source platforms. This has led to a lot of research and analysis of such networks and data that exists in social media. This paper is focused on dynamic analysis of social networks, where snapshots of network are taken at fixed intervals and are analysed on various performance measures. The real time e-mail dataset of a company (Enron) has been evaluated and visualised dynamically. The network measures are evaluated at each timestamp and clustering is performed on that data and its performance is calculated on various measures. Tabu search optimisation algorithm has been used for clustering the timestamped data and a comparison is done between the fixed size cluster and variable size clusters. The results suggests that for certain time stamps the value of precision, recall and f measure for fixed size clusters are better than the variable size clusters. These measures can further be used for the selection of the dynamic clustering techniques for social network analysis.

Dynamic social network analysis and performance evaluation

Dynamic social network analysis and performance evaluation

Evaluating Chaco migration scenarios using dynamic social network analysis

Migration was a key social process contributing to the creation of the ‘Chaco World’ between AD 800 and 1200. Dynamic social network analysis allows for evaluation of several migration scenarios, and demonstrates that Chaco’s earliest ninth-century networks show interaction with areas to the west and south, rather than migration to the Canyon from the Northern San Juan. By the late eleventh century, Chaco Canyon was tied strongly to the Middle and Northern San Juan, while a twelfth-century retraction of networks separated the Northern and Southern San Juan areas prior to regional depopulation. Understanding Chaco migration is important for comprehending both its uniqueness in U.S. Southwest archaeology and for comparison with other case studies worldwide.

When to choose dynamic vs. static social network analysis.

There is increasing interest in using dynamic social networks in the study of animal sociality and its consequences. However, there is a general lack of guidance on the when and how such an approach will be valuable. The aim of this paper is to provide a guide on when to choose dynamic vs. static social network analysis, and how to choose the appropriate temporal scale for the dynamic network. I first discuss the motivations for using dynamic animal social networks. I then provide guidance on how to choose between dynamic networks and the "standard" approach of using static networks. I discuss this in the context of the temporal scale of changes observed, of their predictability and of the data availability. Dynamic networks are important in a number of scenarios. First, if the network data are being compared to independent processes, such as the spread of information or disease or environmental changes, then dynamic networks will provide more accurate estimates of spreading rates. Second, if the network has predictable patterns of change, for example diel cycles or seasonal changes, then dynamic networks should be used to capture the impact of these changes. Third, dynamic networks are important for studies of spread through networks when the relationship between edge weight and transmission probability is nonlinear. Finally, dynamic social networks are also useful in situations where interactions among individuals are dense, such as in studies of captive groups. The use of static vs. dynamic network requires careful consideration, both from a research question perspective and from a data perspective, and this paper provides a guide on how to evaluate the relative importance of these.

A prospective examination of online social network dynamics and smoking cessation.

IntroductionUse of online social networks for smoking cessation has been associated with abstinence. Little is known about the mechanisms through which the formation of social ties in an online network may influence smoking behavior. Using dynamic social network analysis, we investigated how temporal changes of an individual’s number of social network ties are prospectively related to abstinence in an online social network for cessation. In a network where quitting is normative and is the focus of communications among members, we predicted that an increasing number of ties would be positively associated with abstinence.MethodParticipants were N = 2,657 adult smokers recruited to a randomized cessation treatment trial following enrollment on BecomeAnEX.org, a longstanding Internet cessation program with a large and mature online social network. At 3-months post-randomization, 30-day point prevalence abstinence was assessed and website engagement metrics were extracted. The social network was constructed with clickstream data to capture the flow of information among members. Two network centrality metrics were calculated at weekly intervals over 3 months: 1) in-degree, defined as the number of members whose posts a participant read; and 2) out-degree-aware, defined as the number of members who read a participant’s post and commented, which was subsequently viewed by the participant. Three groups of users were identified based on social network engagement patterns: non-users (N = 1,362), passive users (N = 812), and active users (N = 483). Logistic regression modeled 3-month abstinence by group as a function of baseline variables, website utilization, and network centrality metrics.ResultsAbstinence rates varied by group (non-users = 7.7%, passive users = 10.7%, active users = 20.7%). Significant baseline predictors of abstinence were age, nicotine dependence, confidence to quit, and smoking temptations in social situations among passive users (ps < .05); age and confidence to quit among active users. Among centrality metrics, positive associations with abstinence were observed for in-degree increases from Week 2 to Week 12 among passive and active users, and for out-degree-aware increases from Week 2 to Week 12 among active users (ps < .05).ConclusionsThis study is the first to demonstrate that increased tie formation among members of an online social network for smoking cessation is prospectively associated with abstinence. It also highlights the value of using individuals’ activities in online social networks to predict their offline health behaviors.

Exploring classroom interaction with dynamic social network analysis

ABSTRACTThis article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and explain those patterns. Dynamic SNA extends SNA and builds on previous research on network change. Utilizing data from six videos from the TIMSS video study and a convenience sample of five observations of one teacher in a secondary school in the south of England, a methodology for dynamic SNA was applied to classroom interaction data. The results are in two ways relevant for this journal. Firstly, they show that recent developments in SNA might be usefully applied to furthering knowledge about the dynamics of classroom interaction. Secondly, the results provide an example of the use of tablet technology for fieldwork data collection.

Similar but Different: Dynamic Social Network Analysis Highlights Fundamental Differences between the Fission-Fusion Societies of Two Equid Species, the Onager and Grevy's Zebra.

Understanding why animal societies take on the form that they do has benefited from insights gained by applying social network analysis to patterns of individual associations. Such analyses typically aggregate data over long time periods even though most selective forces that shape sociality have strong temporal elements. By explicitly incorporating the temporal signal in social interaction data we re-examine the network dynamics of the social systems of the evolutionarily closely-related Grevy’s zebras and wild asses that show broadly similar social organizations. By identifying dynamic communities, previously hidden differences emerge: Grevy’s zebras show more modularity than wild asses and in wild asses most communities consist of solitary individuals; and in Grevy’s zebras, lactating females show a greater propensity to switch communities than non-lactating females and males. Both patterns were missed by static network analyses and in general, adding a temporal dimension provides insights into differences associated with the size and persistence of communities as well as the frequency and synchrony of their formation. Dynamic network analysis provides insights into the functional significance of these social differences and highlights the way dynamic community analysis can be applied to other species.

Bridge building potential in cross-cultural learning: a mixed method study

Although many international students experience transitional issues, most research assumes that these issues will disappear over time with increased interaction. Using principles of social network theory, this study addressed why some students become bridge builders between international and host students, while others primarily interact with co-national students. In this innovative mixed method study of 81 students from 28 nationalities using dynamic social network analyses combined with embedded case studies of five (potential) bridge builders, the results indicate that students use a range of complex strategies to cope with mixed group work. After 11 weeks, two students stayed as strong bridge builders across groups, two had some bridge building characteristics, and one focussed more on her friends rather than on her assigned group. These findings indicate that even after 3 years of study, international and host students carry on using complex and dynamic social network strategies based on their particular characteristics as well as adaptation to their unique experiences.