The Network Structure of Sociology Production

The Network Structure of Sociology Production

James MoodyOhio State University

Indiana UniversityDecember, 2005

Introduction

Outline:•Big Picture: Networks, Structure, Action & Outcomes

•Guiding Questions & General Approach•Examples: Hierarchy, Romance & the spread of STDs

•Networks & Science: Two Questions & 4 networks•How do scientific fields evolve?•Where do good ideas come from?

•Data Sources & Methods•Results

•Where does sociology fit? Journal co-citation networks•What do sociologists study? Topic networks•Who produces sociology? Social science collaboration networks

•Discussion

Networks, Structure, Action & Outcomes

Where does social structure come from?How does social structure enable & constrain action & outcomes?

General Approach:(1) Seek structure in patterns of association:

“To speak of social life is to speak of the association between people – their associating in work and in play, in love and in war, to trade or to worship, to help or to hinder. It is in the social relations men establish that their interests find expression and their desires become realized.”

— Peter M. Blau Exchange and Power in Social Life, 1964

Guiding Questions:


General Approach:(2) Focus on large-scale network structure:

"If we ever get to the point of charting a whole city or a whole nation, we would have … a picture of a vast solar system of intangible structures, powerfully influencing conduct, as gravitation does in space. Such an invisible structure underlies society and has its influence in determining the conduct of society as a whole."

— J.L. Moreno, New York Times, April 13, 1933

Guiding Questions:



General Approach:(3) Link well-defined network structures to relevant social theory…

“The social structure [of the dyad] rests immediately on the one and on the other of the two, and the secession of either would destroy the whole. . . . As soon, however, as there is a sociation of three, a group continues to exist even in case one of the members drops out.”

—Simmel ([1908] 1950:123)

Guiding Questions:


This can then be operationalized as node-connectivity directly.


General Approach:(4) …in a manner that can explain truly emergent social properties.

“[Social facts] assume a shape, a tangible form peculiar to them and constitute a reality sui generis vastly distinct from the individual facts which manifest that reality”

— Durkheim Rules Of Sociological Method

Guiding Questions:


A Gallery of Friendship Networks

776 adolescents from a working-class, all-white, suburban, school in the Midwest.

(Source: Add Health)

Examples: Hierarchy in High School


A Gallery of Friendship Networks

678 adolescents from a working-class, all-white, rural, school in the Midwest.

(Source: Add Health)

Across these settings (and many more) we can literally see the differences imposed by classic ‘Blau space’ features of youth communities. Race, grades, SES etc. often shape the gross topography of school friendship networks.



Distribution of Popularity

By size and city type

Size

Com

mun

ity ty

pe



If you examine all schools you find:

• All of the school networks have a rank-strata structure

• The structure remains constant even though nearly half of all relationships are new

•People’s position in the popularity distribution is fluid

What social process will explain a stable macro-structure in the face of dynamic relations?



003

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111D

111U

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030C

(3)

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120U

120C

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(5)

300

(6)

Endogenous Building Blocks: A periodic table of social elements:


003

(0)

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(1)

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021U

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(2)

111D

111U

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030C

(3)

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120U

120C

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(5)

300

(6)

Intransitive

Mixed

Transitive

Classic balance theory offers a set of simple local rules for relational change:•A friend of a friend is a friend•My enemy’s enemy is my friend.



003

102

021D

021U

030C

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111U

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021C

vacuous transition

Increases # transitive

Decreases # intransitive

Decreases # transitive

Increases # intransitive

Vacuous triad

Intransitive triad

Transitive triad

(some transitions will both increase transitivity & decrease intransitivity – the effects are independent – they are colored here for net balance)



-0.6

-0.4

-0.2

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Same Race

SES GPA

Both Smoke

College

Drinking

FightSame Sex

Same Clubs

Reciprocity

Transitivity

Intransitivity

Same Grade

ERGM Coefficient Distributions*

Endogenous Focal Orgs. Dyadic Similarity/Distance.

*Coefficients based on pseudo-likelihood approximations, here standardized so they fit well on the page…



Examples: Building Romantic Networks

MaleFemaleMaleFemale

1212 99

63

22


What micro-structures are taboo in high-school romantic relations?




The 4-cycle prohibition fits the observed data.


Networks, Structure, Action & OutcomesExamples: Systemic Effects of Local Action in STD Cores

An STD Puzzle:• contact rates are low (most people have few partners)• dyadic transmission is difficult (compared, say, to the flu)• people are infectious for short periods of time

•Particularly for bacterial STDs, but even AIDS infectiousness peaks shortly after acquiring the disease

How does the disease manage to remain endemic?• Activity heterogeneity is the common answer: a few active “stars” keep the disease endemic•But this doesn’t fit the empirical facts-on-the-ground in many cases. •What if many people make small changes, instead of few people making big changes?

Examples: Systemic Effects of Local Action in STD Cores



While my substantive work has ranged widely, I always focus on the intersection of individual action embedded in network structures over time.

The long-term goal is to identify fundamental principles for either networks or action that can explain the wide variety observed social structures with a small number of locally digestible and contextually relevant action rules.

My new work turns these tools to questions about the development of science.

1) How do scientific fields evolve?a) Is there a coherent logic to the ebb and flow of topics studied?b) How does the success or failure of ideas depend on the social community

in which it is embedded?c) (How) Does the evidentiary basis of a field shape it’s logic of discovery?

The descriptive answer is given by mapping the field in network space.

The analytic answer will come by modeling the emergence, growth and decline of scientific subfields.

Networks & Science: Two Questions & 4 networks

2) Where do good ideas come from?a) What is a good idea?

a) Ideas that change a scientific field. Indexed by (a) citations and (b) the relevant topography of the networks within which the idea was originally embedded. Ideas are not inherently good; they are recognized as “good” by their effect on a field.

b) How do disciplines produce new ideas?a) Intersection Good ideas are produced by combining ideas of others in

unique ways (Burt)b) Development Good ideas arise naturally from either the progressive “error

reduction” process of good normal science (Popper) or the accepted practices of a scientific community (Crane).

c) Peer Influence & Recognition Any idea is a good idea if others think so, and thinking so is influenced by the network. (Gould).

d) Resource competition Search for prestige conditioned by organizational structure (Fuchs)

Will model this by examining how citations are affected by field dynamics (and vice versa).


We are thus left with multiple action frames to guide our understanding:

Truth: Ideas run their error-reduction course (Popper)Prestige: Actors seek the greatest visibility (Merton)Resource competition: “To the victor goes the spoils” – FuchsBoundary Protection ( Lamont)Fractal Development (Abbott)Community Influence (SSK – Collins, etc)Peer magnification (Gould)Power (JL Martin)

For entire fields, these mechanisms are largely unknown and underspecified.

Need to extend beyond particular lab studies Take a large-scale “Satellite” view of science dynamics Link action frames to specific patterns in 4 science networks

Networks & Science: Two Questions & 4 networksTheoretical approaches to scientific development

Four relevant networks:

1. Citation networks – a direct trace of scientific recognition & production

2. Topic networks – clusters of scientific products related to the same subject

3. Collaboration networks – “invisible communities” of social interaction that produces scientific products

4. Research Communities – People linked through common research topics (Substantively a derivative of 2 & 3)

Networks & Science: Two Questions & 4 networksTheoretical approaches to scientific development

Evidentiary Basis: How do we array disciplines with respect to evidence?

Two Dimensions: Objectivity & Control

Objectivity is taken from Popper: The extent to which a given knowledge claim is independent of the knower.

Control refers to the ability of scientists to directly manipulate the object of study. “Lab Science” with complete ability to control apparatus (and thus environment) represents the strongest ability, while “observation” represents the other.

Cases: Chemistry (Lab Science: High Objectivity & High Control)Geology (Field Science: High Objectivity & Low Control)Sociology (Social Science: Moderate Objectivity & Low

Control)Literary Criticism (Humanities: Low Objectivity & Low

Control)

This approach is very similar to Fuchs (1993)

Networks & Science: Two Questions & 4 networksScientific Environments


Chemistry Geology Sociology Literary Criticism

Citation Journal Citation Structure

Topics Subfield Evolution

Collaboration Collaboration & Cohesion

Community

The field of sociology can thus be thought of as the intersection of multiple networks.

The shape of these networks differs across scales and over time.- Differences between local and global visions of the network shape our perceptions of scientific coherence.

- We tend to perceive coherence in our own specialty fields and incoherence for the entire discipline.

- A globally federated structure, that cannot easily exclude empirical topics, might still be socially coherent if scientific mixing cross-cuts empirical problems.

We can see this structure by examining these 4 networks at large scale and over time.

Networks & Science: Two Questions & 4 networksFocusing on Sociology as a current case

•Citation Networks•Compiled from the ISI web of science Journal citation tables•Covers 1681 social science journals indexed in 2003•Will eventually

-fill this series from 1990 to present across all fields.-Add a sample of paper-level citations to model performance.

•Topic & Collaboration Networks (for Sociology) •Compiled from Sociological Abstracts•281,163 papers published between 1963 and 1999

•A sub-sample of “sociology only” papers published in a select set of non-specialty sociology journals 35% of the total (~100K)

•Contains information on title, abstract, keywords, author(s), tables, journal & citation•Will use similar indexes for Chemistry, Geology and Lit Crit

Data Sources

Where does sociology fit?

•Perennial debates over the existence of a theoretical core •Rapid growth in the internal diversity of topics sociologist study:

0

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45

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1950 1960 1970 1980 1990 2000 2010

Number of ASA Sections


•Perennial debates over the existence of a theoretical core •Rapid growth in the number of journals relevant to sociologists:

This growth & diversity has been seen as evidence for the ultimate emptiness of sociology as a scientific discipline.

But disciplines are shaped by the connections between ideas, not the number of ideas.

That is, we recognize fields by who they speak to as much as by what they speak about.

The clearest empirical trace of this communication is citation.

Disciplines can then be defined as clusters of work that speak more to each other than to anyone else, which we trace with co-citation networks.



Building co-citation networks

Links in a co-citation network are constructed by measuring how similar each journal is to every other journal.

Similarity is gauged by correlating the pattern of citations received by each journals from every other journal.

AJS ASR AER … JER

J1

J2

J3

J4 . . .JER

# #

# #

# #

# #

0 0

0 0

0 0

0 # # #

0 0

Comparing across columns tells us whether the two journals are recognized by others as similar.


AJS ASR AER … JER

AJS

ASR

AER . . . JER

1.0

1.0

1.0

1.0

High

MedLow

HighLow Low

This create a valued network of ties between two journals. I use a cosine similarity score developed in bibliometrics, selected for those with ties > 0.45 & at sharing at least 2% of their citation volume.

Source: Loet Leydesdorff

Building co-citation networks

Links in a co-citation network are constructed by measuring how similar each journal is to every other journal.

Similarity is gauged by correlating the pattern of citations received by each journals from every other journal.


Density = 0.197N=152Isolates (not shown): 5

Economics co-citation similarity network

Node size proportional to log(degree)


Political Science co-citation similarity network

Density = 0.160N=69Isolates (not shown): 10Node size proportional to log(degree)


Sociology co-citation similarity network

Density = 0.140N=69

Isolates : 7


•Sociology “fits” at the center of the social sciences. We are not as internally cohesive as Economics or Law, but more so than many (anthropology, allied health fields).

•This represents a tradeoff. We have traded unique dominance of a topic (markets, politics, mind, space, history) for diversity & thus centrality.

•Sociology is an interstitial discipline (Abbott, 2004) in at least two-senses:

•There is no content topic we can reasonably exclude•We pull together, and generate, the ideas and topics covered by specialty disciplines.

•This makes us uniquely positioned to provide insights on many different empirical questions. How have the topics sociologists study shifted over time?

What do sociologists study?

How do we capture the internal organization of research problems?

•Could use paper-level citation networks (see Hargens 2000), but data are difficult & expensive to obtain for large-scale networks.

•Can examine the network of papers formed by the topics they write about.

•Directly taps scientific content•Purely endogenous creation of topics that allows new topic areas to emerge and old ones to die over time•Tractability: data can be extracted from information held in Sociological Abstracts•Multiple levels:

•Coarse grained Focus solely on keywords (Light 2005)•Fine grained Use all information available (title, abstract, keywords)

What do sociologists study?A fine grained view

Data Selection & Manipulation:Index entries contain title, abstract and keywords that summarize the paper’s content.

•Sample all papers indexed within four 3-year windows between 1970 and 1999.

•Construct a paper – by – word matrix, where the ij cell lists how many times word i is used to describe paper j.

•Word set is stemmed to get at root words•A stop-list is used to minimize inclusion of low-information content words (“the” “and” “is” etc.) or words commonly found in the data source (“Tables” “Figure” “References”)

•Construct a network by linking the most highly correlated papers •Use correlation of 0.40 or better•Ties are treated as valued in the network analyses


Analysis & Presentation: General approach is “quantitatively inductive”

- Construct a low-dimensional map of the network, using contour sociograms. These allow for full information in the network structure.

-Use cluster analysis to identify distinct topics-Use a variant of Moody’s RNM algorithm to cluster the network

This clustering routine:(a) is efficient: Allows clustering on 10s of thousands of nodes(b) automatically specifies the optimal number of clusters(c) allows that some cases can fall ‘between’ clusters

-I set a minimum cluster size of 12 papers published over the 3-year window.

-Evaluate the clustered papers for content and label the maps.


Analysis & Presentation: General approach is “quantitatively inductive”

Compare the maps over time qualitatively, looking for general changes in the frequency & alliance of topics.

Examine shifts in structural indicators of the extent of clustering & cluster size distributions.


Example: One-step neighborhood of “More information, better jobs?”

A fine grained view

What do sociologists study?A fine grained view: Content(all journals)


The cluster content of the topic network has evolved slowly:

•Some clearly central specialties have remained prominent over the entire period. This includes larger areas such as:

• Class & Stratification• Race & Ethnicity• Education• Gender (Strongest from 1980s on)• Family (Strongest from the 1980s on)• Crime

As well as clearly distinct, though numerically smaller bodies of research related to

• Suicide• Sociology of Science, Technology & “Reflexive” sociology• Unions

A fine grained view: Content(all journals)


The cluster content of the topic network has evolved slowly:

•The clearest change has been the rapid growth of social research on health.•Dominated by a very large body of research related to HIV/AIDS

•Other areas of relative growth include:•Family topics were most prominent in the 1980s•A strong presence of research on sex & sexuality emerged in the 1980s and 90s

•Relative declines have come in areas such as:• Groups• Interaction• “Radical” studies• Elite studies

Summary: A move away from basic social processes toward studying social problems, with a growing uniqueness of theory & method

A fine grained view: Content(all journals)

What do sociologists study?A fine grained view: Content(Restricted Sample)


The cluster content of the restricted topic network has evolved similarly to the wider social science field:

•The subfield structure is less dominated by the purely applied work on HIV/AIDS in the 90s, but there is a still a clear association of topics around sexuality, health and AIDS.

•Health, Family, Education, Gender, and Race are always prominent and large.

•The relative prominence of “reflexive sociology” is much higher –•These topics cannot be published elsewhere, and the resulting tight cluster looks proportionately larger in the smaller sample.

A fine grained view: Content(Restricted Sample)


We can measure the degree of consensus in words used to describe papers with:

C = pi2

Where pi is the proportion of times word i is used

A fine grained view: Content


0.11

0.115

0.12

0.125

0.13

1965 1970 1975 1980 1985 1990 1995 2000

Word Consensus Scores1970 - 1999

C (x

100

)

All SA Journals

Soc Only

A fine grained view: Content


Proportion of papers falling inside a cluster

A fine grained view(Core Soc)

0.10.20.30.40.50.60.70.80.9

1

1965 1970 1975 1980 1985 1990 1995 2000

Cn > 12

Cn > 100

Total

Total

Restricted

Restricted


We can measure the extent that ties fall within clusters with the modularity score:

Where:s indexes clusters in the networkls is the number of lines in cluster sds is the sum of the degrees of sL is the total number of lines

s

ss

Ld

LlM

2

2


0.7

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Network Modularity1970 - 1999

Mod

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ity S

core

All SA Journals

Soc Only


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Number of Clusters1970 - 1999

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Soc Only


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Mean Cluster Size1970 - 1999

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Soc Only


The cluster structure of the topic network:

•The vast majority of papers can be assigned to clear clusters, with slight growth in this proportion over time.

•The number of clusters has increased rapidly, though slightly slower within core sociology than in the broader field of social science.

•There has been significant growth in the tails of the distribution – the size distribution is more skewed in later periods.

•The modularity of the network has increased over time, though most of this change is between the 1970 and 1980 periods.

A fine grained view


Next steps:1. Build a continuous moving window to fill in the dates from 1970 to

2005.2. Link clusters across time periods, so we can track exactly the relative

growth and decline of each subfield.3. Model this growth as a function of connections to other fields, author

composition and disciplinary environment.4. Build this network’s dual: Scientists connected through topics.


A clustered topic structure focused strongly on practical problem solving has a hint of Durkheim’s concern: Is there any integration across these topic clusters?

We shouldn’t jump too quickly to the fractured conclusion:• Topic clusters are formed from papers, and papers typically have well

encapsulated ideas. They have a small “maximum digestible unit”

• Scientific integration is really about how scientists bridge these multiple topics.• If authors write and collaborate across these topics then, ideas can

quickly disseminate as well.

What is the structure of the collaboration graph – if this is highly clustered it would signal potential fragmentation Who produces sociology?

Who produces sociology?

Science is typically produced through collaboration, both formally and informally (Crane 1972, Crane & Small 2000, Friedkin 1998).

The best empirical trace of collaboration for large communities of science is coauthorship.

•Misses the less intense collaborations recognized in acknowledgements, discussions, colleagues reading each other’s work

•But should provide the strongest test of a fractionalization hypothesis, since the set of people we write with should be more like us than the set of people we have lunch with or discuss work with informally.

•There are differences across subfields in formal collaboration rates, which, if anything, should magnify the extent of observed fragmentation.

Coauthorship Trends in SociologySociological Abstracts and ASR

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Distribution of Coauthorship Across JournalsSociological Abstracts, 1963-1999

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Coauthorship Rank

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thor

AJS

Soc.Forces

ASR

Soc.Theory

Signs

AtcaPolitica

J. Soc.History

J.Am.Statistical A.

J. Health &Soc. Beh.

Child Development


Construct a collaboration network by assigning an edge between any pair of people who coauthored a paper together.


Example Paths: 3-steps from Stan Wasserman

N=361


Example Paths: 3-steps from Stan Wasserman

Construct a collaboration network by assigning an edge between any pair of people who coauthored a paper together.

N=361Node size proportional to log of degree

•High relative probability that a node’s contacts are connected to each other.•Small relative average distance between nodes

C=Large, L is Small = SW Graphs


The simplest summary test for a fragmented network is to measure the extent of clustering in the network. Watts’ work on the “small-world problem” suggests that if the collaboration network is a small world network it might be fractured.

In a highly clustered, ordered network, a single random connection will create a shortcut that lowers L dramatically

Watts demonstrates that Small world properties can occur in graphs with a surprisingly small number of shortcuts


Locally clustered graphs are a good model for coauthorship when there are many authors on a paper.

Paper 1 Paper 2 Paper 3 Paper 4 Paper 5

Newman (2001) finds that coauthorship among natural scientists fits a small world model.


I test this model on the sociology coauthorship network, using all authors from 1963 – 1999.

The sociology network is less clustered than would be expected by chance and somewhat longer overall distances.

This suggests that it does not have a small-world structure.

Observed Random

Clustering

Distance

0.194 0.206

9.81 7.57


The network has a broad Core-periphery structure

29,462Bicomponent

38,823

59,866

(68,923)

Component

Unconnected

Structurally Isolated



Health

General Sociology

Internal Structure of the Coauthorship Core

•Strong specialty effects for ever-coauthoredUnlikely:

History & TheorySociology of KnowledgeRadical / Marxist SociologyFeminist / Gender Studies

Likely:Social psychologyFamilyHealth & MedicineSocial ProblemsSocial Welfare


•Weak specialty effects for network embeddedness•Large number of coauthors increases embeddedness•Large number of people on any given paper decreases embeddedness


Evolution of Network Cohesion: 5-year moving window

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0.25

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1975 1980 1985 1990 1995 2000

Year

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ent

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nect

ivity

ConnectivityBicomponent

Component


Summary & discussion

Social Science Citation Structure•Economics, Law, Psychology, Business/Management, Linguistics are most cohesive

•The are also “peripheral” in that they speak to a relatively limited set of problems

•Sociology is at least as cohesive as Political Science, and more cohesive than fields such as Anthropology, Social Work, Education or allied health fields that all have more limited empirical domains

•Our position represents a tradeoff between internal cohesion and external centrality.

Scientific Topic Network•Big-Picture: A general progression towards problem solving and the specialization of work on theory & methods (Light 2005).

•Fine-grained structure: •A federated topic structure that has remained largely constant since the 1970s, though there have been shifts in topics.

•Key content areas have remained largely constant•Race, Family, Class, Gender, Science, and Health

•A decrease in focus on general foundation problems•Group structure, community, interaction

•An increase in work on social problems•Health & HIV/AIDS -related topics

•Some evidence for greater homogeneity in topics discussed


Scientific Collaboration Network•The networks is not divided into small research-area based clusters.

•There is no partition that strongly separates scientists.•This has to imply that authors bridge topic clusters. •This is good for social cohesion, and probably good for theoretical cohesion.

•Caveat: There is evidence for a division based on research method, with largely quantitative work more likely to be coauthored, though there is no such simple division in the topics network.


Combined, these models suggest a discipline that is integrated socially and locally cohesive topically.

Discipline-wide integration will likely only increase as pressures for collaboration push more scientists to work together across topics.

However, the perception of disintegration will likely continue:• because most of us are only exposed outside our areas by work that appears in the general journals. •But almost all of the topical cohesion is due to “normal science” work occurring in specialty journals.


The Network Structure of Sociology Production

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