Social Network Theory vs. Physical Sciences

Types of ties
In the physical sciences, it is not unusual to regard any dyadic phenomena as a network. In this usage, a network and a mathematical graph are synonymous, and a common set of techniques is used to analyze all instances,
from protein interactions to coauthorship to international trade. In contrast, social scientists typically distinguish among different kinds of dyadic links both analytically and theoretically. For example, the typology divides dyadic relations into four basic types—similarities, social relations, interactions, and flows. Much of social network research can be seen as working out how these different kinds of ties affect each other.





 The importance of structure.
As in the study of isomers in chemistry, a fundamental axiom of  social network analysis is the concept that structure matters. For example, teams with the same composition of member skills can perform very differently depending on the patterns of relationships among the members. Similarly, at the level of the individual node, a node’s outcomes and future characteristics depend in part on its position in the network structure.Whereas traditional social research explained an individual’s outcomes or characteristics as a function of other characteristics of the same individual (e.g., income as a function of education and gender), social network researchers look to the individual’s social environment for explanations, whether through influence processes (e.g., individuals adopting their friends’
occupational choices) or leveraging processes (e.g., an individual can get certain things done because of the connections she has to powerful others). A key task of social network analysis has been to invent graph-theoretic properties that characterize structures, positions, and dyadic properties (such as the cohesion or connectedness of the structure)  and the overall “shape” (i.e., distribution) of ties.

Research questions
In the physical sciences, a key research goal has been formulating universal characteristics of nonrandom networks, such as the property of having a scale-free degree distribution. In the social sciences, however, researchers have tended to emphasize variation in structure across different groups or contexts, using these variations to explain differences in outcomes. For example, Granovetter argued that when the city of Boston sought to absorb two neighboring towns, the reason that one of the towns was able to successfully resist was that its more diffuse
network structure wasmore conducive to collective action. 

Theoretical mechanisms
Perhaps the most common mechanism for explaining consequences of social network variables is some form of direct transmission from node to node.Whether this is a physical transfer, as in the case of material resources such as money (35), or a mimetic (imitative) process, such as the contagion of ideas, the underlying idea is that something flows along a network path from one node to the other.

Stephen P. Borgatti, et al., Network Analysis in the Social Sciences, Science 323, 892 (2009)


About Kyoung Jun Lee
Professor of Kyung Hee Univ.

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