Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences. Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available. It is one (...) of the developing, cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society. The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings. Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts. (shrink)

Social network analysis (SNA) is an increasingly popular approach that provides researchers with highly developed tools to map and analyze complexes of social relations. Although a number of network scholars have explicated the assumptions that underpin SNA, the approach has yet to be discussed in relation to established philosophies of science. This article argues that there is a tension between applied and methods-oriented SNA studies, on the one hand, and those addressing the social-theoretical nature and implications of (...) networks, on the other. The former, in many cases, exhibits positivist tendencies, whereas the latter incorporate a number of assumptions that are directly compatible with core critical realist views on the nature of social reality and knowledge. This article suggests that SNA may be detached from positivist social science and come to constitute a valuable instrument in the critical realist toolbox. (shrink)

Network analysis is a fundamental approach to the study of social structure. This chapter traces its development, distinguishing characteristics, and analytic principles. It emphasizes the intellectual unity of three research traditions: the anthropological concept of the social network, the sociological conception of social structure as social network, and structural explanations of political processes. Network analysts criticize the normative, categorical, dyadic, and bounded-group emphases prevalent in many sociological analyses. They claim that the most direct way to (...) study a social system is to analyze the pattern of ties linking its members. By analyzing complex hierarchical structures of asymmetric ties, they study power, stratification, and structural change. (shrink)

In this chapter, we discuss social network analysis as a method for the history of economics. We argue that social network analysis is not primarily a method of data representation but foremost a method of discovery and confirmation. It is as such a promising method that should be added to the toolbox of the historian of economics. We furthermore argue that, to be meaningfully applied in history, social network analysis must be complemented with historical (...) knowledge gained by other means and often by more traditional, mostly qualitative, methods. It should therefore be viewed as a method that complements rather than replaces more established approaches in the history of economics. (shrink)

We present social network analysis as a complement to other methods in the history of economics. We first discuss why social network analysis is especially promising for the study of the history of recent economics. We then use an example of research using it to highlight some of its characteristics.

Competitive state anxiety is an integral feature of sports performance but despite its pervasiveness, there is still much debate concerning the measurement of the construct. Adopting a network approach that conceptualizes symptoms of a construct as paired associations, we proposed re-examining competitive state anxiety as a system of interacting components in a dataset of 485 competitive athletes from the United Kingdom. Following a process of data reduction, we estimated a network structure for 15 items from the modified Three (...) Factor Anxiety Inventory using the graphical LASSO algorithm. We then examined network connectivity using node predictability. Exploratory graph analysis was used to detect communities in the network and bridge expected influence calculated to estimate the influence of items from one community to items in other communities. The resultant network produced a range of node predictability values. Community detection analysis derived three communities that corresponded with previous research and several nodes were identified that bridged these communities. We conclude that network analysis is a useful tool to explore the competitive state anxiety response and we discuss how the results of our analysis might inform the assessment of the construct and how this assessment might inform interventions. (shrink)

This paper examines the motivation theory of Rowley and Moldoveanu (2003) on shareholder activism in the context of shareholder resolution networks. Shareholder resolution filings occur both within subnetworks as well as across subnetworks, indicating these motivations are mixed. I extend the motivational issue by also examining the response of the corporation to such activism. That resolutions might migrate from identity to interest motivated groups is examined as an element of future research.

We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter‐relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and (...) reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects. (shrink)

Morphological EvoDevo is a field of biological inquiry in which explicit relations between evolutionary patterns and growth or morphogenetic processes are made. Historically, morphological EvoDevo results from the coming together of several traditions, notably Naturphilosophie, embryology, the study of heterochrony, and developmental constraints. A special feature binding different approaches to morphological EvoDevo is the use of formalisms and mathematical models. Here we will introduce anatomical network analysis, a new approach centered on connectivity patterns formed by anatomical parts, with (...) its own concepts and tools specifically designed for the study of morphological EvoDevo questions. Riedl’s concept of burden is tightly related to the use of anatomical networks, providing a nexus between the evolutionary patterns and the structural constraints that shape them. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of (...) the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities (...) highly versus lowly. The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics. (shrink)

Network analysis views complex systems as networks with well-defined structural properties that account for their complexity. These characteristics, which include scale-free behavior, small worlds and communities, are not to be found in networks such as random graphs and lattices that do not correspond to complex systems. They provide therefore a robust ground for claiming the existence of “complex networks” as a non-trivial subset of networks. The theory of complex networks has thus been successful in making systematically explicit relevant (...) marks of complexity in the form of structural properties, and this success is at the root of its current popularity. Much less systematic has been, on the other hand, the definition of the properties of the building components of complex networks. The obvious assumption is that these components must be nodes and links. Generally, however, the internal structure of nodes is not taken into account, and links are serendipitously identified by the perspective with which one looks at the network to be analyzed. For instance, if the nodes are Web pages that contain information about scientific papers, one point of view will match the relevant links with hyperlinks to similar Web pages, and another with citations of other articles. We intend to contribute here a systematic approach to the identification of the components of a complex network that is based on information theory. The approach hinges on some recent results arising from the convergence between the theory of complex networks and probabilistic techniques for content mining. At its core there is the idea that nodes in a complex network correspond to basic information units from which links are extracted via methods of machine learning. Hence the links themselves are viewed as emergent properties, similarly to the broader structural properties mentioned above. Indeed, beside rounding up the theory, this approach based on learning has clear practical benefits, in that it makes networks emerge from arbitrary information domains. We provide examples and applications in a variety of contexts, starting from an information-theoretic reconstruction of the well-known distinction between “strong links” and “weak links” and then delving into specific applications such as business process management and analysis of policy making. (shrink)

We have considered a simple word game called the word-morph. After making our participants play a stipulated number of word-morph games, we have analyzed the experimental data. We have given a detailed analysis of the learning involved in solving this word game. We propose that people are inclined to learn landmarks when they are asked to navigate from a source to a destination. We note that these landmarks are nodes that have high closeness-centrality ranking.

In the last few decades, increasing attention has been given to gender issues in urban design. However, research on the urban environment continues to show large gender inequalities, which are especially evident when studying the use and enjoyment of the public space. This study aims to identify predominant patterns of use in public places and to explore the possible existence of traditional gender roles in the urban space. The study uses, three public spaces in the city of Barcelona as a (...) case study, an innovative combination of systematic observation techniques and network analysis procedures. Variables collected by EXOdES, a dedicated software analysis tool for systematic observation, are represented as nodes of a network system and analyzed using network analysis tools. Findings confirmed that, in spite of the progressive consolidation of feminist urbanism, uses in the public realm resulting from traditional gender roles remained explicitly recognizable. Whereas women’s occupation of space was related to playground and resting areas, generally involving care activities concerned with children or elderly people, men were primarily located in resting and sports areas, practicing sports, or participating in leisure activities. These patterns of use were more prone to emerge when users were part of a group than when they were alone. From a gender perspective, a contribution of the study is that it informed about main aspects of the analyzed public spaces reconfirming the existence of traditional roles in society, and the significance of exploring the public space as a key scenario where social features are explicitly exposed. From a methodological perspective, the processing of observational data with network analysis tools proved to be relevant and suitable for dealing with the intricacies of urban place analysis. Compared to more classical approaches and systems, these techniques allowed to identify and interpret complex systems composed of many variables and relationships in a relatively straightforward manner, which turns it into a useful aid for urban designers and architects. (shrink)

BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography and functional magnetic resonance imaging. However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. (...) Fused priori weighted minimum norm estimation with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders. (shrink)

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old (...) Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation rehabilitation training and (...) the other group received BCI combined with FES training. We constructed functional networks in both groups of patients based on direct directed transfer function and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: after rehabilitation training, the Fugl–Meyer assessment scale score was significantly improved in the BCI–FES group, and there was no significant difference in the FES group. Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention, and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group. These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training. (shrink)

Network analysis is increasingly appreciated as a methodology in the social sciences. In recent years, it is also receiving attention among historians of science. History of economics is no exception in that researchers have begun to use network analysis to study a variety of topics, including collaborations and interactions in scientific communities, the spread of economic theories within and across fields, or the formation of new specialties in the discipline of economics. Against this backdrop, a debate (...) is emerging about how network analysis can help address questions that are pertinent to the history of economics. With this paper, we want to push this debate one step forward by offering and discussing five reasons why network analysis should have a future in the history of economics. (shrink)

Agri-food clusters have generated great interest in recent years and prompted a new wave of research dedicated to ‘Localized Agri-Food Systems’. However, the specific nature of relations between firms who belong to SYALs has rarely been studied. Our purpose is to show how the analysis of company directors’ advice networks helps to better understand the specificity and innovative dynamics of SYALs. Our research was based on a case study in the Biterrois wine growing region of southern France. We conducted (...) a survey to collect information on advice relations between wine cooperative managers, who play a key role in the innovation process toward quality wine. Sociometric analysis of the advice relations, coupled with interviews, led to three main results: the first was that managers simultaneously cooperate on technical projects and compete for business ends. The second was a correlation between the innovations implemented by the cooperatives and the degree of centrality of the managers in the networks, revealing the relational and local conditions of innovations in SYALs. The third was that managers can modify their own network to improve the competitiveness of both their own cooperative and of the SYAL as a whole. In conclusion, we show that advice network analysis is an appropriate tool to describe the relational dimension of SYALs, to better understand their process of innovation, and to help managers improve the collective strategies they apply within the SYAL. (shrink)

Local food systems are growing, and little is known about how the constellation of farms and markets change over time. We trace the evolution of two local food systems over six years, including a dataset of over 2690 market connections between 1520 locations. Longitudinal social network analysis reveals how the architecture, actor network centrality, magnitude, and spatiality of these supply chains shifted during the 2012–2018 time period. Our findings demonstrate that, despite growth in the number of farmers’ (...) markets, grocery stores, farms and restaurants in both counties, each local food system also experienced high turnover rates. Over 80% of the market connections changed during the study period. Farms, farmers’ markets, and grocery stores showed a 40–50% ‘survival’ rate, indicating their role in sustaining local food systems over longer time periods. Other actors, such as restaurants, had a much higher turnover rate within the network. Both food systems became more close-knit and consolidated as the center of gravity for both local food systems pulled away from urban areas toward rural farmland. Evidence of both growth and decay within local food systems provides a new understanding of the social networks behind local food markets. (shrink)

Social network research is widely considered atheoretical. In contrast, in this article I argue that network analysis often mixes two distinct theoretical frameworks, creating a logically inconsistent foundation. Relationalism rejects essentialism and a priori categories and insists upon the intersubjectivity of experience and meaning as well as the importance of the content of interactions and their historical setting. Formalism is based on a structuralist interpretation of the theoretical works of Georg Simmel. Simmel laid out a neo-Kantian program (...) of identifying a priori categories of relational types and patterns that operate independently of cultural content or historical setting. Formalism and relationalism are internally consistent theoretical perspectives, but there are tensions between them. To pave the way for stronger middle-range theoretical development, I disaggregate the two approaches and highlight the contradictions that must be addressed or resolved for the construction of any general and inclusive theory. (shrink)

BackgroundPsychological distress is common among infertility patients. Total scale scores are often used to represent the severity of anxiety, depression, or stress, which ignores important differences between specific symptoms, and relationships between symptoms. This study aimed to identify patterns of psychological distress experienced by infertility patients and to identify the most central symptoms of anxiety, depression, and stress.MethodFrom June to September 2016, 740 infertility patients were included in this cross-sectional study. Infertility patients were asked to complete the Generalized Anxiety Disorder-7, (...) Patients Health Questionnaire-9 (PHQ-9), and Fertility Problem Inventory. Network analysis was used to examine the patterns of psychological distress in infertility patients and to test the most central symptoms of anxiety, depression, and stress.ResultsRestlessness was the most central symptom in infertility patients. “Feelings of guilt” had the highest strength among PHQ-9 symptoms. “Relationship concern stress” and “sexual concern stress” had the strongest connections in the network. Stability estimation indicated that the order of node strength centrality was more stable than the order of closeness and betweenness (the CS-coefficients were 0.75, 0.13, and 0.67, respectively). In addition, network structure and global strength were invariant across gender.LimitationsThe cross-sectional design did not permit identification of causal relationships. Patients in this study were recruited from one reproductive hospital; especially, most patients had low socioeconomic status, which limits generalizability of the findings.ConclusionThis study reinforces the need to better understand the underlying causes of psychological distress in infertile patients. A more detailed investigation of the relationship between these symptoms could provide information for psychosocial interventions aimed beyond “alleviating psychological distress.” We should consider the individual psychological symptom pattern and its potential causes in infertility patients instead of assuming a consistent psychological distress structure. (shrink)