NETWORK ANALYSIS IN LARGE SOCIAL SYSTEMS: SOME THEORETICAL AND METHODOLOGICAL PROBLEMS

1979 ◽  
pp. 379-402 ◽  
Author(s):  
Edward O. Laumann
Keyword(s):  
Network Analysis ◽  
Social Systems ◽  
Methodological Problems

scholarly journals Towards a process-driven network analysis

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Mareike Bockholt ◽  
Katharina Anna Zweig
Keyword(s):  
Network Analysis ◽  
Viral Infections ◽  
Early Stage ◽  
Social Systems ◽  
Transportation Systems ◽  
Actual Usage ◽  
Network Representation ◽  
Traded Goods ◽  
Network Methods ◽  
Network Process

Abstract A popular approach for understanding complex systems is a network analytic one: the system’s entities and their interactions are represented by a graph structure such that readily available methods suitable for graph structures can be applied. A network representation of a system enables the analysis of indirect effects: if A has an impact on B, and B has an impact on C, then, A also has an impact on C. This is often due to some kind of process flowing through the network, for example, pieces of informations or viral infections in social systems, passenger flows in transportation systems, or traded goods in economic systems. We argue that taking into account the actual usage of the system additionally to the static network representation of the system can yield interesting insights: first, the network representation and applicable network methods cannot be chosen independently from the network process of interest (Borgatti 2005; Dorn et al. 2012; Zweig 2016; Butts 2009). Therefore, focussing on the relevant network process in an early stage of the research project helps to determine suitable network representations and methods in order to obtain meaningful results (we call this approach process-driven network analysis). Second, many network methods assume that the spreading of some entity follows shortest or random paths. However, we show that not all flows are well approximated by this. In these cases, incorporating the network usage creates a real addition of knowledge to the static aggregated network representation. Note This is an extended and revised version of a conference article (Bockholt and Zweig 2019), published and presented at COMPLEX NETWORKS 2019.

scholarly journals Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Leonie Neuhäuser ◽  
Felix I. Stamm ◽  
Florian Lemmerich ◽  
Michael T. Schaub ◽  
Markus Strohmaier
Keyword(s):  
Network Analysis ◽  
Measurement Errors ◽  
Social Systems ◽  
Systematic Bias ◽  
Model Framework ◽  
Network Analyses ◽  
External Node ◽  
Systematic Biases ◽  
Node Attributes ◽  
Attributed Networks

AbstractNetwork analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.

scholarly journals Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany

2021 ◽  
Vol 9 ◽  
Author(s):  
Corinna Coupette ◽  
Janis Beckedorf ◽  
Dirk Hartung ◽  
Michael Bommarito ◽  
Daniel Martin Katz
Keyword(s):  
United States ◽  
Network Analysis ◽  
Legal System ◽  
Social Systems ◽  
The United States ◽  
Single Type ◽  
Modern World ◽  
Legal Documents ◽  
Original Dataset ◽  
Over Time

How do complex social systems evolve in the modern world? This question lies at the heart of social physics, and network analysis has proven critical in providing answers to it. In recent years, network analysis has also been used to gain a quantitative understanding of law as a complex adaptive system, but most research has focused on legal documents of a single type, and there exists no unified framework for quantitative legal document analysis using network analytical tools. Against this background, we present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998–2019). Our framework provides tools for assessing the size and connectivity of the legal system as viewed through the lens of specific document collections as well as for tracking the evolution of individual legal documents over time. Implementing the framework for our dataset, we find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes. This holds regardless of whether we measure the systems at the macro, the meso, or the micro level.

scholarly journals Resilience beyond Formal Structures: A Network Perspective towards the Challenges of an Aging Workforce in the Oil and Gas Industry

2019 ◽  
Vol 5 (1) ◽  
pp. 15 ◽  
Author(s):  
Fabio Bento ◽  
Luciano Garotti
Keyword(s):  
Network Analysis ◽  
Management Practices ◽  
Explicit Knowledge ◽  
Oil And Gas ◽  
Initial Conditions ◽  
Social Systems ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Knowledge Loss ◽  
Logic Network

Changes in workplace demographics in the oil and gas industry have raised a concern about the risks of a knowledge-loss crisis due to mass retirement. The industry response has often consisted of strategies aimed at mapping knowledge across organizational units, codifying knowledge in databases, and mentoring new staff. However, such common managerial responses show important limitations in terms of grasping tacit and network-based dimensions of knowledge in complex oil production operations. Therefore, there is an industrial need for innovative knowledge management practices. In this conceptual article, we look at the knowledge-loss crisis from the perspective of network resilience in complex systems. A central assumption here is that it is important to look at retiring staff not only in terms of their explicit knowledge, but also in relation to their roles in evolving networks of interactions. Why do some social systems adapt to the departure of some individuals, recover from eventual knowledge-loss crises, and keep performing its functions? From an anticipatory logic, network analysis may show the initial conditions of a system and identify possible loss scenarios. From an adaptive logic, network analysis may inform interventions aimed at facilitating processes of interactions from which new knowledge may emerge and spread. Integrated operations may be a step in this direction.

scholarly journals Usage of Social Network Analysis (Sna) in Crisis Management

2016 ◽  
Vol 2 (1) ◽  
pp. 72-88
Author(s):  
Hakan Güreşci ◽  
Recep Sait Arpat
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Crisis Management ◽  
Social Systems ◽  
Holistic Approach ◽  
Current Crisis ◽  
Critical Data ◽  
Political Crises ◽  
Management Concepts

Abstract Social systems are complex structures that consist of different sub-systems. Therefore, understanding social systems is more difficult than comprehending electronic or mechanical systems. What makes social systems more complex than other systems is that society is not simply the sum of each individual in the society. In the current global system, the countries, which have become small villages, try to meet national security needs by converting the unknown to known and identifying the correlation among political, military, social and economic events. The current crisis management concepts are conducted through systematic approaches. Besides, the management of social, economic and political crises need to be conducted in a holistic approach covering all sub-systems. At this point, the function of Social Network Analysis (SNA) emerges. SNA, which forms the main subject of this paper, is a tool for examining the structure of a crisis through correlating the sub-elements. The aim of this study is to show how SNA can be used in crisis management. First, SNA is performed on a generic crisis situation and the results are presented. Then, the additional critical data requirements are put forward to manage the crisis effectively.

Visualization and Analysis of Frames in Collections of Messages

2012 ◽  
pp. 321-339 ◽  
Author(s):  
Esther Vlieger ◽  
Loet Leydesdorff
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Vector Space ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Social Systems ◽  
Semantic Map ◽  
Network Space ◽  
Communication Of Meaning

A step-by-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs, or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). The authors distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered a generated as an architecture by the network of relations in the network space; words are then not only related, but also positioned. These positions are expected rather than observed, and therefore one can communicate meaning. Knowledge can be generated when these meanings can recursively be communicated and therefore also further codified.

Social Network Analysis

2018 ◽  
pp. 388-415
Author(s):  
Soufiana Mekouar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Communication Systems ◽  
Information Diffusion ◽  
Social Systems ◽  
Critical Role ◽  
Network Applications ◽  
Graph Measure ◽  
Statistical Graph

The study of social network analysis has grown in popularity in the past decades and has been used in many areas. It is an interesting and useful field that gained an increasing popularity due to the explosion of social media that has emerged with advances in communication systems, which play a critical role in forming human activities and interactions in social systems. The authors present some techniques from a data mining perspective and statistical graph measure that can be used in various applications such as to perform community detection, clustering in a social network, identify spurious and anomalous users, predict links between vertices in a social network, model and improve the information diffusion, design trust models, and improve other applications. Then, the authors provide a recent literature review of such applications and thus outline challenges of social network applications.

Themes, syntax and other necessary steps in the network analysis of texts: a research paper

1996 ◽  
Vol 35 (4) ◽  
pp. 657-665 ◽  
Author(s):  
Carl W. Roberts ◽  
Roel Popping
Keyword(s):  
Network Analysis ◽  
Qualitative Analysis ◽  
Text Analysis ◽  
Research Paper ◽  
Network Characteristics ◽  
Methodological Problems ◽  
Research Questions ◽  
Network Encoding

Recent approaches to the qualitative analysis of texts afford visual depictions of words as networks. Yet network characteristics can also be quantified, enabling one to draw probabilistic inferences about a population of texts from a sample of texts-encoded-as-networks. This article describes three types of ambiguity (and related methodological problems) that arise during three necessary steps in the quantification of texts as networks: idiomatic ambiguity (in the identification of themes [or nodes]); illocutionary ambiguity (in the identification of syntactic links [or arcs]); and relevance ambiguity (in the identification of network characteristics). As one moves from theme to syntax to network, not only does one add complexity to one's conclusions, but one also adds complexity to the encoding process as distinct types of linguistic ambiguity must be resolved. The added complexity of network encoding will be unnecessary for most research questions - questions that might better be addressed via thematic or semantic text analysis.

scholarly journals Social network analysis for social neuroscientists

2020 ◽  
Author(s):  
Elisa C Baek ◽  
Mason A Porter ◽  
Carolyn Parkinson
Keyword(s):  
Network Analysis ◽  
Human Brain ◽  
Social Environment ◽  
Social Systems ◽  
Social Neuroscience ◽  
New Work ◽  
Social Environments ◽  
Individual Cognition ◽  
The Brain ◽  
Advance Knowledge

Abstract Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to advance knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individual’s social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementations of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.

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