Network Analysis in the Social Sciences

Stephen P. Borgatti, et al.

Science 323, 892 (2009));

History

In the fall of 1932, there was an epidemic of runaways at the Hudson School for Girls

Moreno's SociometryNetwork Analysis in the Social Sciences

The links in SN provided channels for the flow of social influence among girlsComte -The idea of social physics

Durheim -Societes like biological systems

1940-1950

Matrix algebra-Graph theoryGroup network lab (MIT)-Study of network

topology (stars vs circles)-Bavelas et alSmall world problem

Six degress of separationUrbanization destroyed community

Network analysis to represent community structure

1960-1970 S. F. Nadel began

to see societies not as monolithicentities but rather as a “pattern or

network (or ‘system’) of relationshipsobtaining between actors in

their capacity of playing roles relativeto one another”

Levi-Strauss, scholarsbegan to represent kinship systemsas relational algebras that consistedof a small set of generating relations

That gave hope to the idea thatdeep lawlike regularities might underlie the apparent chaos of

human social systems

Bott (The study of 20 urban families)Degree of segregation in the role relationship of

husband and wife The more connected the network, the

more likely the couple would maintain a traditionalsegregation of husband and wife roles,

showing that the structure of the larger networkcan affect relations and behaviors within the dyad.

(TOP-DOWN approach)

Social capital theoryThe idea that whom a person is

connectedto, and how these contacts are connected

to each other, enable people to access resources that ultimately

lead them to such things as better jobs and

faster promotions.e.g LINKEDIN SOCIAL NETWORK

1980-1990 In the 1990s, network analysis radiated

into a great number of fields, including physics and biology,management consulting

public health and crime/war fighting. Knowledge management,

where the objective is to help organizationsbetter exploit the knowledge and capabilities distributed

across its members. In public health, network

approaches have been important both instopping the spread of infectious diseases and in

providing better health care and social support. Network approach in war and crimes

Social Network Theory

Perhaps the oldest criticism of social network research is that the field lacks a (native) theoretical understanding—it

is “merely descriptive”or “just methodology.”

Types of ties.Social scientists

typically distinguish among different kinds of dyadic links both analytically and theoretically.

The importance of structure

Teams with the same composition of member skills can perform very

differently depending on the patterns of relationships among the members A node’s outcomes and future characteristics depend in part on its

position in the network structure Connections to powerfull others

Centrality

The potential power that an actor mightwield due to the ability to slow down flows or to distort what is

passed along in such a way as to serve the actor’s interests A node’s positionin a network determines in part the

opportunities and constraints that it encounters, and in this way plays an important role in a node’s outcomes.

CENTRALITY-POWER -INFLUENCE

Homogeneity and performance

• Something flows along a network path from one node to the other

The adaptation mechanism states that nodesbecome homogeneous as a result of experiencing and adapting to similar

social environments If two nodes have ties to the same (or

equivalent) others, they face the same environmental forces and are likely to

adapt by becoming increasingly similar

The exclusion mechanism

The exclusion mechanism refers to competitive situations in which one

node, by forming a relation with another, excludes a third node

A foreseeable challenge for network research in the social sciences is

that its theories can diffuse through a population,influencing the way people see

themselves and how they act, a phenomenon that Giddens described

as the double-hermeneutic

Economic Networks

Research examining economic networks has been studied from two perspectives; one view comes from economics and sociology; the other originated in research on complex systems in physics and computer

science Micro-macro analysis

A star-spoke network, like a very centralized or-ganization, in which a

central “hub” channels allcommunication among agents. In this “micro” perspective we focus on the individual system elements and their

detailed network of relations

The micro analysis of economic networks

relies on game theory, whichaims at identifying Nash equilibria (i.e.,situations that are strategically stable inthe sense that no agent has an incentiveto deviate). It can also rely on operations

research, where algorithms forsearching and optimizing have beendeveloped. As the number of nodes

and possible links scales up, however,such problems become very difficult

to solve, and classical approaches areunsatisfactory.

Small changes in environmental volatility can have drastic consequences in the overall configuration

of the systemThe inability of previous approaches to reproduce

statistical regularities that have been observedempirically in network structures justifies

our pursuit of a complex-systems approach thatmay provide predictions for large-scale networks.

Characteristic features of theagents

Degree of connectivity (numberof links) or their centrality, as measured

on the basis of the importance of a node—which, in turn, can be affected

by its links to other nodes

Complex nertworks Thus, instead of focusing on understanding

the endogenous behavior of individual agents, the complex-systems approach centers on understanding

how the network-formation rules systematicallyaffect the emerging link structure

Networks generated with different stochasticalgorithms, such as random, scale-free or smallworld

networks, have been compared with realcomplex networks

Comparing network structures across these differentdisciplines suggests that economic networks

may also reflect a similar universality

Links “weight” In the complex-network context, “links” are not binary

(existing or not existing), but are weighted according to the economic interaction.

Country centrality in the terms ofthe likelihood that any given additionaldollar traded in the world reaches that

country by following existing linkswith a probability proportional to its

weigh ,the relative changes in centralityover time show trends for different

countries that predict divergence inregional integration within the global economy

and do so better than traditional international tradeand macroeconomic statistics

Focus on centrality or other suchproperties of networks can only provide a

firstorder classification that emphasizes the role of fluctuations and randomness and cannot

predict the underlying dynamics of the agents, whether

they are firms or countries Massive data analysis

Time and space (going beyond snapshot approach)

Structure identification Beyond simplicity

Systemic feedback