![]() Here, we focus on so-called sociocentric network studies. A graph shows these connections visually, as illustrated by Figures 1– 4. A matrix indicates relationships between every person and every other person by coding numbers indicating the existence or nature of relationships in a square table. Network data may be fruitfully represented in matrix or graphical form. Edges and arcs are often measured on a binary (presence/absence) scale, but may also be valued (e.g., how well two people know each other or how much they like each other). A directed relationship is one such as between two friends in which A identifies B as a friend but B does not reciprocate. ![]() Examples of undirected relationships include spouses and siblings. Social ties may be described as “edges” (undirected relationships between nodes) or “arcs” (directed relationships from one node to another). In discussing network effects, it is helpful to refer to “egos,” or the individuals under study, and their “alters,” or the people to whom they are connected (though the same person may be an ego and an alter from different perspectives). Social network ties are not restricted to friends, of course, and one may be connected to one’s spouse’s brother’s friend, or one’s co-worker’s friend’s sister, and so on. For example, a person is one degree removed from her friend, two degrees removed from her friend’s friend, three degrees removed from her friend’s friend’s friend, and so on. Within a network, one can speak of the “distance” between two people (also known as the “geodesic distance” or “degree of separation”), which is the shortest path in the network from one person to another. ![]() Once all the nodes and ties are known, one can draw pictures of the network and discern every person’s location within it, placing each individual in social space analogous to geographic space. Social networks consist of two elements: individuals (nodes) and the social ties between them. Creating visual images of networks can serve important heuristic purposes in both research and policy, and visual images are powerful complements to quantitative analyses. Social network analysis also promises to provide targets for intervention, by identifying influential individuals, by identifying cliques of at-risk individuals, or by elucidating procedures for maximizing the impact of health interventions. The scientific objectives in social network analysis are generally 1) to understand the processes that determine the topology, or structure, of the network, and 2) to understand the extent and mechanisms behind any inter-personal effects within the network. Health-related phenomena, whether germs or information or behaviors, can diffuse widely within social networks. New work with social networks suggests that such interpersonal effects extend beyond just those individuals to whom a person is directly connected. Indeed, social networks affect health through a variety of mechanisms, including: (1) provision of social support, (2) social influence ( e.g., norms, social control), (3) social engagement, (4) person-to-person contacts ( e.g., pathogen exposure), and (5) access to resources ( e.g., money, jobs, information). ![]() It is not just how connected a person is, but also who a person is connected to, and what those people are doing, that has an effect. In addition, and distinctly, they are influenced by behaviors and outcomes in people who are “nearby” them in the network (including their friends, friends of friends, and so on). People are thus affected by their location in a social network. A person with more friends and social contacts generally has better health than a person with fewer friends, and a person at the center of a network is more susceptible to both the benefits and risks of social connection (e.g., for infectious disease) than those at the periphery of a network.
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