Social Networks: How networks may impact your life - Networks and their hidden influence

Description:

We're all embedded in vast social networks of friends, family, co-workers and more. Nicholas Christakis tracks how a wide variety of traits -- from happiness to obesity -- can spread from person to person, showing how your location in the network might impact your life in ways you don't even know.

Nicholas Christakis explores how the large-scale, face-to-face social networks in which we are embedded affect our lives, and what we can do to take advantage of this fact.

Why you should listen to him:

People aren't merely social animals in the usual sense, for we don't just live in groups. We live in networks -- and we have done so ever since we emerged from the African savannah. Via intricately branching paths tracing out cascading family connections, friendship ties, and work relationships, we are interconnected to hundreds or thousands of specific people, most of whom we do not know. We affect them and they affect us.

Nicholas Christakis' work examines the biological, psychological, sociological, and mathematical rules that govern how we form these social networks, and the rules that govern how they shape our lives. His work shows how phenomena as diverse as obesity, smoking, emotions, ideas, germs, and altruism can spread through our social ties, and how genes can partially underlie our creation of social ties to begin with. His work also sheds light on how we might take advantage of an understanding of social networks to make the world a better place.

At Harvard, Christakis is a Professor of Medicine, Health Care Policy, and Sociology, and he directs a diverse research group investigating social networks. His popular undergraduate course (Life and Death in the US) is podcast [available on itunes]. His book, Connected, co-authored with James H. Fowler, appeared in 2009, and has been translated into nearly 20 languages. In 2009, he was named by Time magazine to its annual list of the 100 most influential people in the world, and also byForeign Policy magazine to its list of 100 top global thinkers.

Nicholas Christakis: The hidden influence of social networks, TED2010, Filmed Feb2010

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Background

A social network is a social structure made of individuals (or organizations) called "nodes," which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.

Social network analysis views social relationships in terms of network theory consisting of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.

In its simplest form, a social network is a map of all of the relevant ties between all the nodes being studied. The network can also be used to measure social capital -- the value that an individual gets from the social network. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.

Social network analysis

Social network analysis (related to network theory) has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, and sociolinguistics, and has become a popular topic of speculation and study. People have used the idea of "social network" loosely for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt, Kathleen Carley, Martin Everett, Katherine Faust, Linton Freeman, Mark Granovetter, David Knoke, David Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network analysis software, and researchers. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They typically either study whole networks (also known as complete networks), all of the ties containing specified relations in a defined population, or personal networks (also known as egocentric networks), the ties that specified people have, such as their "personal communities". The distinction between whole/complete networks and personal/egocentric networks has depended largely on how analysts were able to gather data. That is, for groups such as companies, schools, or membership societies, the analyst was expected to have complete information about who was in the network, all participants being both potential egos and alters. Personal/egocentric studies were typically conducted when identities of egos were known, but not their alters. These studies rely on the egos to provide information about the identities of alters and there is no expectation that the various egos or sets of alters will be tied to each other. A snowball network refers to the idea that the alters identified in an egocentric survey then become egos themselves and are able in turn to nominate additional alters. While there are severe logistic limits to conducting snowball network studies, a method for examining hybrid networks has recently been developed in which egos in complete networks can nominate alters otherwise not listed who are then available for all subsequent egos to see. The hybrid network may be valuable for examining whole/complete networks that are expected to include important players beyond those who are formally identified. For example, employees of a company often work with non-company consultants who may be part of a network that cannot fully be defined prior to data collection.

(Wikipedia)