<abstract><title style='display:none'>Abstract</title>Social network analysis has been one of the most influential scientific revolutions of the past century. Its success has been due, in part, to its methodological sophistication and the emphasis it places on identifying and clearly depicting features of social structure. As such, social network analysis is often viewed in stark contrast to the structuralist paradigm that dominated the social sciences prior to its rise – structural–functionalism - in the mid-20th century. In this paper, we highlight important connections that exist between the key assumptions of social network analysis and the key tenets of some of the most influential structural-functional theories – especially those of Robert K. Merton and Talcott Parsons and their collaborators and followers. We reveal a substantial affinity between some of their most influential ideas and contemporary analysis of social network <italic>dynamics</italic>, in particular, and several ways in which their work could inform promising advances in this line of research. Our ultimate goal is to highlight the prospect of using these theories to guide future analyses of the dynamics of large social systems and the sequences of real-time action that compose them.</abstract>

If Parsons had Pajek: The Relevance of Midcentury Structural-Functionalism to Dynamic Network Analysis*

Book Review: <italic>The Oxford Handbook of Social Networks</italic>

Inferential Network Analysis

<abstract><title style='display:none'>Abstract</title>Bonacich (1987) suggested a family of centrality measures that provide a useful way of modeling questions of power and network constraint. However, the literature offers little guidance regarding the choice of β, the parameter which alters the way the measure accounts for the effect of having powerful contacts in ones network. In this paper I explore the way the choice of the β parameter affects the power indices the Bonacich measure generates. I consider three network properties which might affect the way the choice of β influences the Bonacich power indices. I find that in high density networks with few internal ‘chains’ and few pendants, the choice of β is largely immaterial. Conversely, in sparse networks, those with a high proportion of pendant nodes, or those with many chains, the value of β has a substantial effect on the power indices the measure generates. Next I consider whether power indices produced by interior values of β might be represented as a linear combination of “pure” vectors, those generated with values of β at either end of the parameter range and β = 0. I find that in the vast majority of cases a linear combination of “pure” vectors power is equivalent to using indices produced by interior values of β, making the choice of β largely moot. Finally, in the unlikely case that this disaggregation is inappropriate, I discuss the question of determining an appropriate value of β empirically.</abstract>

Choosing the ‘β’ Parameter When Using the Bonacich Power Measure

Book Review: <italic>Multimodal Political Networks</italic>

<abstract><title style='display:none'>Abstract</title>That social networks play a central role in religious life is well accepted by most social scientists. We are reasonably confident, for instance, that they are crucial for the recruitment and retention of members, the diffusion of religious ideas and practices, motivating individuals to volunteer and become politically active, the health and well-being of people of faith, and conflict, radicalization, and (sometimes) violence. However, in conference presentations, journal articles, and books social network analysts have shown little interest in exploring the interplay of networks and religion. In this paper, I review, and in some cases expand upon, what social scientists of religion have learned about networks and religion. I conclude with a call for social network analysts to focus the analytical tools of social network analysis on a phenomenon that has and continues to exert considerable influence in today’s world.</abstract>

Networks and Religion: Ties that Bind, Loose, Build Up, and Tear Down

<abstract>
<title style='display:none'>Abstract</title>
Centrality measures are widely utilized in complex networks to assess the importance of nodes. The choice of measure depends on the network type, leading to diverse node rankings. This paper aims to compare various centrality measures by examining their correlations. We specifically focus on the Pearson correlation coefficient and Spearman correlation. Pearson correlation considers node centrality values, while Spearman correlation is based on node ranks. Our study encompasses different network topologies, including random, scale-free, and small-world networks. We investigate how these network structures influence correlation values. The main part of the paper describes the relationship between correlations and network model parameters. Additionally, we explore the impact of global network characteristics on correlations, as well as their direct connection to network parameters. Through a systematic review of literature-based centrality measures, we have identified and selected the most commonly employed ones to investigate their correlation including degree centrality, betweenness centrality, eigenvector centrality, and closeness centrality. Our findings reveal that correlations in random networks are minimally affected by network structure, whereas restructuring significantly impacts correlations in other networks. In particular, we show a notable impact of structural parameter variations on correlations within small-world networks. Furthermore, we demonstrate the substantial influence of fundamental network characteristics such as spectral gap, global efficiency, and majorization gap on correlations. We show that amongst the various properties, the spectral gap stands out as the most valuable indicator for estimating correlations.
</abstract>

An Analysis of Correlation and Comparisons Between Centrality Measures in Network Models

Longitudinal Network Models

A Research Agenda for Social Networks and Social Resilience

Personal Networks: Classic Readings and New Directions in Egocentric Analysis

Trade and Nation: How Companies and Politics Reshaped Economic Thought

Living in Networks: The Dynamics of Social Relations

<abstract><title style='display:none'>Abstract</title>Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation, early warning, and faster response to both positive and negative organizational activities. By applying statistical process control techniques to social networks we can rapidly detect changes in these networks. Herein we describe this methodology and then illustrate it using four data sets, of which the first is the Newcomb fraternity data, the second set of data is collected on a group of mid-career U.S. Army officers in a week long training exercise, the third is the perceived connections among members of al Qaeda based on open source, and the fourth data set is simulated using multi-agent simulation. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.</abstract>

Detecting Change in Longitudinal Social Networks

<abstract><title style='display:none'>Abstract</title>We use balance theoretic ideas to study the dynamics of the international system of nations in a network of signed relations from 1946 through 1999. Using the Correlates of War data for this period, we apply pre-specified signed blockmodeling to characterize the fundamental structure of this network. Even though the system expanded greatly with many ties being created and/or destroyed, the basic structure remained the same but with new positions being added over time. The blockmodels generated temporal measures of imbalance, as did the counts of imbalanced triples. Regardless of using the line index of imbalance or the number of imbalanced 3-cycles, the results provided decisive evidence contradicting the balance theoretic hypothesis of signed networks moving towards balanced states. Structural balance theory remains very useful by pointing to the more important study of how and why signed networks move towards and away from balance at different points over time. Some major methodological problems for studying signed networks, regardless of whether they involve nations or human actors, were raised and addressed. Proposals for future research are suggested for modeling and understanding the dynamics of signed networks.</abstract>

Structural Balance and Signed International Relations

<abstract><title style='display:none'>Abstract</title>Our goal in this paper is to explore two generic approaches to disrupting dark networks: kinetic and non-kinetic. The kinetic approach involves aggressive and offensive measures to eliminate or capture network members and their supporters, while the non-kinetic approach involves the use of subtle, non-coercive means for combating dark networks. Two strategies derive from the kinetic approach: Targeting and Capacity-building. Four strategies derive from the non-kinetic approach: Institution-Building, Psychological Operations, Information Operations and Rehabilitation. We use network data from Noordin Top’s South East Asian terror network to illustrate how both kinetic and non-kinetic strategies could be pursued depending on a commander’s intent. Using this strategic framework as a backdrop, we strongly advise the use of SNA metrics in developing alterative counter-terrorism strategies that are contextdependent rather than letting SNA metrics define and drive a particular strategy.</abstract>

Strategies for Combating Dark Networks

<abstract><title style='display:none'>Abstract</title>In this paper, we employ archival materials from multiple institutional sources to reconstruct the dynamic network of interorganizational collaboration that emerged in response to the Hurricane Katrina disaster of late 2005. Over the period from initial storm formation through the first week following landfall in Louisiana, we record active participation by over 1,500 organizations in response activities. We here conduct an exploratory analysis of the growth and evolution of the network of collaboration among responding organizations, an identification of organizations that emerged as central actors in the response process, and the cohesive subgroups that crystallized within the larger network. Finally, we conclude with a discussion of several issues related to the use of archival methods in research on interorganizational networks in disaster settings, and to the use of automated methods for network extraction.</abstract>

Interorganizational Collaboration in the Hurricane Katrina Response*

Open Access Agreement