Network science in the context of "Street network"

Audience theory offers explanations of how people encounter media, how they use it, and how it affects them. Although the concept of an audience predates modern media, most audience theory is concerned with people’s relationship to various forms of media. There is no single theory of audience, but a range of explanatory frameworks. These can be rooted in the social sciences, rhetoric, literary theory, cultural studies, communication studies and network science depending on the phenomena they seek to explain. Audience theories can also be pitched at different levels of analysis ranging from individuals to large masses or networks of people.

James Webster suggested that audience studies could be organized into three overlapping areas of interest. One conceives of audiences as the site of various outcomes. This runs the gamut from a large literature on media influence to various forms of rhetorical and literary theory. A second conceptualizes audiences as agents who act upon media. This includes the literature on selective processes, media use and some aspects of cultural studies. The third see the audiences as a mass with its own dynamics apart from the individuals who constitute the mass. This perspective is often rooted in economics, marketing, and some traditions in sociology. Each approach to audience theory is discussed below.

A white-collar worker is a person who performs knowledge-based, aptitude-based, managerial, or administrative work generally performed in an office or similar setting. White-collar workers include job paths related to banking, finance, compliance, legal, risk management, internal audit, data privacy, cybersecurity, insurance, government, consulting, academia, accountancy, business and executive management, customer support, design, economics, science, technology, engineering, market research, human resources, operations research, marketing, public relations, real estate, information technology, networking, healthcare, architecture, and research and development.

In contrast, blue-collar workers perform manual labor or work in skilled trades; pink-collar workers work in care, health care, social work, or teaching; green-collar workers specifically work in the environmental sector; and grey-collar jobs combine manual labor and skilled trades with non-manual or managerial duties.

A social network is a social structure consisting of a set of social actors (such as individuals or organizations), networks of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities along with a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine dynamics of networks. For instance, social network analysis has been used in studying the spread of misinformation on social media platforms or analyzing the influence of key figures in social networks.

Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations". Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s. Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science.

In mathematics, computer science, and network science, network theory is a part of graph theory. It defines networks as graphs where the vertices or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.

Network theory has applications in many disciplines, including statistical physics, particle physics, computer science, electrical engineering, biology, archaeology, linguistics, economics, finance, operations research, climatology, ecology, public health, sociology, psychology, and neuroscience. Applications of network theory include logistical networks, the World Wide Web, Internet, gene regulatory networks, metabolic networks, social networks, epistemological networks, etc.; see List of network theory topics for more examples.

Social contagion involves behaviour, emotions, or conditions spreading spontaneously through a group or network. The phenomenon has been discussed by social scientists since the late 19th century, although much work on the subject was based on unclear or even contradictory conceptions of what social contagion is, so exact definitions vary. Some scholars include the unplanned spread of ideas through a population as social contagion, though others prefer to class that as memetics. Generally social contagion is understood to be separate from the collective behaviour which results from a direct attempt to exert social influence.

Two broad divisions of social contagion are behavioural contagion and emotional contagion. The study of social contagion has intensified in the 21st century. Much recent work involves academics from social psychology, sociology, and network science investigating online social networks. Studies in the 20th century typically focused on negative effects such as violent mob behaviour, whereas those of the 21st century, while sometimes looking at harmful effects, have often focused on relatively neutral or positive effects like the tendency for people to take action on climate change once a sufficient number of their neighbours do.

Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. Dynamic networks are a function of time (modeled as a subset of the real numbers) to a set of graphs; for each time point there is a graph. This is akin to the definition of dynamical systems, in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of vertices.