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Social Networks Lecture 4: Collection of Network Data & Calculation of Network Characteristics U. Matzat. Course design. Aim: knowledge about concepts in network theory, and being able to apply them, in particular in a context of innovation and alliances

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Social NetworksLecture 4:Collection of Network Data&Calculation of Network CharacteristicsU. Matzat

Course design
Course design

  • Aim: knowledge about concepts in network theory, and being able to apply them, in particular in a context of innovation and alliances

    • Introduction: what are they, why important …

    • Small world networks

    • Four basic network arguments

    • Kinds of network data (collection) & measurement

    • Business networks

    • Assignment 1

Course outlook today
Course outlook - today

4. Methods

  • Kinds of network data: collection (Part I)

  • Typical network concepts: calculation, UCINET software, visualisation (Part II)

    Later: Assignments

    - complete network analysis

    - ego-centered network analysis

Part 1 collection of network data
Part 1 – Collection of Network Data

  • in traditional surveys a random sample of units (e.g. managers) is interviewed

  • properties of individuals are correlated to analyze some phenomena (e.g., correlation of age with openness for new ideas)

  • focus on distributions of qualities of the individuals, not on their relations

  • traditional assumption: sampled units (e.g., managers) are independent of each other and not related to each other

  • inappropriate for SNA

  • traditional survey instruments had to be adjusted & new ones had to be developed

Collection of network data t wo main approaches within sna
Collection of Network Data:two main approaches within SNA

1.) ego-centered network analysis: network (of a specific type) from the perspective of a single actor (ego)

2.) complete network analysis: the relations (of a specific type) between all units of a social system are analyzed

  • the first approach rests on an extension of traditional survey instruments

  • can be combined with random sampling

  • statistical data analyses possible with standard software (e.g., SPSS)

  • the second approach is new

  • (usually) cannot be combined with random sampling

  • quantitative case study

  • statistical data analyses with specialized software (e.g., UCINET)

Ego centered network data
Ego-centered network data

random sample:

  • selection of units (e.g. individuals) out of a population

  • inclusion of one individual does not influence whether another one is also included

  • relationship between units is no criterion of selection

  • respondent (ego) mentions for a relationship of a certain type (e.g. friendship relation) other individuals (alteri) with whom he is related

  • usually the alteri are not within the sample

  • respondent gives additional information about -some characteristics of the alteri (age etc.)

    -the relations between the alteri

    crucial: specialized items for the generation of alteri: name-generator

Ego centered network data the generation of data via name generators
Ego-centered network data: the generation of data via name generators

name generator for reconstruction of friendship networks in a general population:

first step:

  • "From time to time people discuss questions and personal problems that keep them busy with others. When you think about the last 6 months - who are the persons with whom you did discuss such questions that are of personal importance for you.

    Please mention only the first name of the individuals."

  • [If respondent mentions less than five names, ask once more: "Anybody else? " Write down only the first five names.]

    second step:-characterization of alteri (gender, age, etc) and relation between ego and alteri (e.g., strength of relation)

    third step: -characterization of relation between the different pairs of alter (e.g., strength of relation)

Ego centered network data example reconstruction of university company relationships
Ego-centered network data: example: reconstruction of university-company relationships

  • random sample of university researchers

  • question of interest: how does a researcher’s network look like that brings him into contact with business representatives for collaboration?

  • reconstruction of four parts of the network from the point of view of the researcher:

    • within university- within own faculty

    • within university- outside own faculty

    • outside university – within business world

    • [outside university – personal friends, acquaintances etc.]

Example reconstruction of university company relationships
example: reconstruction of university-company relationships

Questionnaire items

Let us suppose that you are convinced that you have an idea, a product or something similar, in which collaboration with a business firm is a sensible and reasonable option.

Do you have any contacts that could be of substantial value for bringing you in touch with a business firm?

0 yes

0 no (continue with question xx)

Example reconstruction of university company relationships1
example: reconstruction of university-company relationships

From which of the employees within your faculty do you expect that they can make a substantial contribution with respect to getting you in contact with business firms that might become partners? Mention the most important persons, at most four.

From which of the employees outside your faculty but within your university do you expect that they can make a substantial contribution with respect to getting you in contact with business firms that might become partners? Mention the most important persons, at most four.

Example cont
Example (cont)

You mentioned up to 16 names of persons. Please write down the name of the first person mentioned, the second person mentioned, the third person mentioned, etc, until every name is on this list. Make sure that each name is mentioned once and only once.

Please carefully check this list. Are any persons missing of whom you feel that – given the questions – they should be included in this list? Persons who are crucial in getting cooperation between you and a business partner going?

If yes, please add these persons to the list (at most two extra persons) and briefly describe your relation to this person.

Example cont second step
Example (cont): second step

We would like to know how strong your relation with the persons in this list is. A strong relation would be a relation with frequent contact and with a regular exchange of information.

Example cont third step
Example (cont): third step

Finally, we would like to ask you about the relations between the listed persons in your network.

Start with the first person in the list. Consider the relation between this person and the other persons in the list. Choose between:

S: strong relation

D: distant relation

0: no relation

Fill out an X if you cannot judge the relationship.

Ego centered network data data matrix
ego-centered network data: data matrix


name generator for three best friends (of two respondents)

gender age friend 1 existing? friend 2 existing? friend 3 existing? tie strength 1 tie strength 1-2 gender friend 1

respondent 11 30 1 1 1 0.8 1 1

respondent 22 40 1 1 0 0.7 0 2




Ego centered network data data matrix2
ego-centered network data: data matrix

  • standard data matrix that can be analyzed with the conventional techniques and conventional software (e.g., SPSS, STATA etc)

  • but special type of variables of the data set

    • some variables describe the respondent

    • some variables describe the respondent's contacts

    • some variables describe the relation between the respondent and his contacts

    • some variables describe relations between members of the respondent's (primary) network

  • these variables can be used to construct other variables that describe properties of the respondent’s network (size, density etc)

  • you have to construct these variables: e.g. via “TRANSFORM – COMPUTE” in SPSS

Ego centered network data1
ego-centered network data

  • ego-centered network data necessary for testing of typical network theories

  • Example: structural holes hypothesis (ego=company)

  • “Innovating companies tend to profit more from new product ideas the more structural holes they have in their collaboration networks with other companies.“

  • a test of this hypothesis is impossible with traditional surveys of companies

Ego centered network data strengths and weaknesses
ego-centered network data: Strengths and weaknesses

+ random sampling possible

+ generalization to a well-defined population possible

+ for the social scientist easy to use techniques of data analysis

- restriction to those parts of the network that are directly visible to the respondent: the primary network; other characteristics of the network are not taken into account

Complete network data1
Complete network data

  • example: network of informal communication between employees of a project group consisting of 5 persons:

  • Mr Smith, Mr Jackson, Mr. White, Mrs Moneypenny, Mrs Brown

  • questionnaire item for Mr Smith:

  • "With whom of the following persons do you now and then chat during a normal working day?" Do you talk with…

  • Mr. Jackson 0 yes0 no Mr. White 0 yes 0 noMrs Moneypenny 0 yes 0 no Mrs Brown 0 yes 0 no

  • question is presented to all members of the project group

  • you need to have a complete list of the names of all units (e.g. individuals) of the social system (e.g. project group) beforehand

Complete network data sociomatrix
Complete network data: sociomatrix

  • the data matrix is different from the traditional data matrix

  • every cell ij in the matrix provides information about the relation between units i and j ("from row i to column j")

  • relation can be symmetric or asymmetric, valued or dichotomous

Complete network data2
Complete network data:

  • collection of complete network data impossible for large random samples

  • necessary for many hypotheses that make predictions about structural effects:"In groups with a high network density the diffusion of innovations takes place more quickly than in groups with a low density."

  • hypothesis can only be tested with complete network data

  • data matrix of complete network data cannot be analyzed with the conventional data analysis techniques

  • specialized software that offers special techniques is needed (e.g., UCINET)

  • you can calculate network characteristics of actors and of the whole network

  • you can calculate network characteristics (within UCINET) for actors that can be exported and then combined with other data (e.g., SPSS data)

Complete network data strengths and weaknesses
Complete network data: Strengths and weaknesses

+ all aspects of the structure of relationships between all actors in a social system are taken into account

  • no random sampling, therefore no generalizations are possible, rather: quantitative case study approach

    - other techniques of data analysis necessary

Part ii calculation visualisation of network concepts 1 in and outdegree
Part II: Calculation & visualisation of network concepts (1): in- and outdegree

For complete, valued, directed network data with N actors, and relations from actor i to actor j valued as rij , varying between 0 and R.

Centrality and power: outdegree (or: outdegree centrality)

For each actor j: the number of (valued) outgoing relations, relative to the maximum possible (valued) outgoing relations.

OUTDEGREE(i) = j rij / N.R

Centrality and power: indegree (or: indegree centrality)

same, but now consider only the incoming relations

NOTE1: this is a locally defined measure, that is, a measure that is defined for each actor separately

NOTE2: this gives rise to several global network measures, such as (in/out)degree variance

NOTE3: if your network is not directed, indegree and outdegree are the same and called degree

NOTE4: these measures can be constructed in SPSS; no need for special purpose software. Try this yourself!

Network measures 2 number of ties of a certain quality
Network measures (2): number of ties of a certain quality

1 = I do not know who this is

2 = I know who it is, but never talked to him/her

3 = I have spoken to this person once or twice

4 = I talk to this person regularly

5 = I talk to this person often

Number of ties:

For each network or for each actor, the number of ties above a certain threshold

(say, all ties with a value above 3)

Number of weak ties (remember Mark Granovetter?):

For each network or for each actor, the number of ties above and below a certain threshold

(say, only ties with values 2 and 3)

Try creating this one yourself in SPSS (try using ‘recode’)

Network measures 3 closeness
Network measures (3): closeness

Centrality and power again: closeness

= Average distance to all others in the network

Note: a shortest path from i to j is called a “geodesic”

Define distance Dij from i to j as:

* Minimum value of a path from i to j

For every actor i, average distance = j Dij / N


Network measures 4 the most common global network property
Network measures (4): the most common global network property


(J. Coleman: “Dense networks provide social capital.”)

For each network: the number of (valued) relations, relative to the maximum possible number of (valued) relations.

= i,j rij / N (N-1) R (directed, valued ties)

NOTE: normally only of use if your data consist of multiple networks

(alliance networks in different sectors or countries / friendship

networks in school classes / …)

NOTE: this is still doable in SPSS

Network measures 5 subgroup models cohesion
Network measures (5): propertySubgroup Models (Cohesion)

  • aim: description of cohesive subgroups within the larger network

  • general and common idea: a subgroup has a certain degree of cohesiveness (direct ties, strong ties)

  • can also be used to make predictions about the diffusion of innovations according to the cohesion model (which pairs of actors influence each other?)

    • which companies constitute a subgroup within the network?

    • which companies are in many subgroups?

    • how many subgroups do exist?

Subgroups some general terminology you need to know
Subgroups: Some general terminology you need to know….. property


  • if a path exists between 2 nodes then these nodes are called reachable 

  • path length

  • number of lines of a path (dichotomous data)

  • example: path length 4213 = 3

    geodesic distance between two nodes

  • there can be more than one path between two nodes, the different paths can have different lengths

  • d(i,j)=length of the shortest path between two nodes i and j

  • example: 4213 = 3, d(i,j)=3 if there exists no shorter path between i and j

  • d(i,j)= if i,j are not reachable


Subgroups terminology
Subgroups: Terminology.... property

completeness of a graph

  • a graph is complete if all pairs of nodes (i,j) are reachable with d(i,j)=1


  • a graph is connected if for every pair (i,j) d(i,j)<


  • a subgraph Gs consists of a subset NsN and its lines Ls L that connect all {i,j}  Ns


  • a subgraph is maximal with respect to some property (e.g., maximal with regard to completeness) if that property holds for the subgraph, but does no longer hold if any additional node and the lines incident with the node are added


Subgroups example: propertymaximal completeness



maximal complete subgraph Gs

Ns={1,2,3,4,5} and the ties between them






Subgroup definitions for undirected dichotomous ties
Subgroup Definitions for undirected dichotomous ties property


a cliques is a maximal complete subgraph that consists of at least three nodes

2 7


3 4

                5      6

Which cliques?

{1,2,3}, {1,3,5}, {3,4,5,6}

cliques can overlap, a clique can not be part of a larger clique because of the maximality conditionimpossible to calculate with SPSS!

Network measures 6 structural holes

This was covered in the 3 propertyrd lecture

Network measures (6): Structural holes

Ron Burt: “Structural holes create value”












  • Robert will do better than

  • James, because of:

  • informational benefits

  • “tertius gaudens” (entrepreneur)

  • autonomy



Network measures 6 structural holes1
Network measures (6): propertyStructural holes

  • Burt, R.S. (1995)

  • NOTE: structural holes can be defined on ego-networks!

    Burt splits his structural holes measure in four separate ones:

    • [1] effective size

    • [2] efficiency (= effective size / total size)

    • [3] constraint (degree to which ego invests in alters

      who themselves invest in other alters of ego)

    • [4] hierarchy (adjustment of constraint, dealing with

      the degree to which constraint on ego is

      concentrated in a single actor)

Structural holes effective size efficiency

A property







Structural holes: Effective size & efficiency

We calculate effective size and efficiency for actor G

(note: because this is an ego-network, all would be different if we would have chosen, for instance, actor A)

Or, the same but a bit easier: Effective size = size - average degree of ego’s alters in ego’s network (excluding ties to ego).


6 - {3 (A) + 2(B) + 0(C) + 1(D) + 1(E) + 1(F)}/6 = 6 - 1.33 = 4.67

Defining constraint actors must divide their attention

A property































































Defining constraint: actors must divide their attention

The assumption is that actors can only invest a certain amount of time and energy in their contacts, and must divide the available time and energy across contacts.

If not explicitly measured, we assume all contacts are invested in equally.

Constraint property

Actor i is constrained in his relation with j to the extent that:

[a] i invests in another contact q who …

[b] invests in i’s contact j

Total investment of i in j =

Pij + q (piq pqj)

“Since this also equals i’s lack of

structural holes, constraint

of i in j is taken to equal”

( Pij + q (piq pqj) )2







Calculating constraint using matrices 1
Calculating constraint using matrices (1) property

c1 c2 c3 c4 c5 c6 c7

r1 0 .25 0 0 .25 .25 .25

r2 .333 0 0 .333 0 0 .333

r3 0 0 0 0 0 0 1

r4 0 .5 0 0 0 0 .5

r5 .5 0 0 0 0 0 .5

r6 .5 0 0 0 0 0 .5

r7 .17 .17 .17 .17 .17 .17 0

Adjacency matrix P =

(see two slides ago) all investment from i in j in 1 step

c1 c2 c3 c4 c5 c6 c7

r1 .37575 .0425 .0425 .12575 .0425 .0425 .33325

r2 .05661 .30636 .05661 .05661 .13986 .13986 .24975

r3 .17 .17 .17 .17 .17 .17 0

r4 .2515 .085 .085 .2515 .085 .085 .1665

r5 .085 .21 .085 .085 .21 .21 .125

r6 .085 .21 .085 .085 .21 .21 .125

r7 .22661 .1275 0 .05661 .0425 .0425 .52411

Matrix product

P2 = P*P =

all investments from i in j in 2 steps

Calculating constraint using matrices 2
Calculating constraint using matrices (2) property

c1 c2 c3 c4 c5 c6 c7

r1 .37 .29 .04 .12 .29 .29 .58

r2 .38 .30 .05 .38 .13 .13 .58

r3 .17 .17 .17 .17 .17 .17 1

R4 .25 .58 .08 .25 .08 .08 .66

r5 .58 .21 .08 .08 .21 .21 .62

r6 .58 .21 .08 .08 .21 .21 .62

r7 .39 .29 .17 .22 .21 .21 .52

P + P2 =

All investments from i to j in 1 or 2 steps

Pij + q (piq pqj)


= 0.444

Etc …

c1 c2 c3 c4 c5 c6 c7

r1 .141 .085 .002 .015 .085 .085 .340

r2 .151 .093 .003 .151 .019 .019 .339

r3 .028 .028 .028 .028 .028 .028 1

r4 .063 .342 .007 .063 .007 .007 .444

r5 .342 .044 .007 .007 .044 .044 .390

r6 .342 .044 .007 .007 .044 .044 .390

r7 .157 .088 .028 .051 .045 .045 .274

Hadamard matrix product (P+P2)2h

= P+P2 squared element wise

Constraint(i,j) can be read from this matrix

Calculating constraint using matrices 3
Calculating constraint using matrices (3) property

Total constraint for actor i =

sum of all constraints Cij with ji

c1 c2 c3 c4 c5 c6 c7

r1 .141 .085 .002 .015 .085 .085 .340

r2 .151 .093 .003 .151 .019 .019 .339

r3 .028 .028 .028 .028 .028 .028 1

r4 .063 .342 .007 .063 .007 .007 .444

r5 .342 .044 .007 .007 .044 .044 .390

r6 .342 .044 .007 .007 .044 .044 .390

r7 .157 .088 .028 .051 .045 .045 .274

= 0.755 <- Constraint(1)

= 0.779 <- Constraint(2)

= 1.173 <- Constraint(3)

= 0.934 <- Constraint(4)

= 0.879 <- Constraint(5)

= 0.879 <- Constraint(6)

= 0.691 <- Constraint(7)

Hierarchy property

  • = degree to which constraint is concentrated in a single actor

  • Cij = constraint from j on i (as on previous pages)

  • N = number of contacts in i’s network

  • C = sum of constraints across all N relationships

  • Hierarchy (i)

  • Minimum = 0 (all i’s constraints are the same)

  • Maximum = 1 (all i’s constraint is concentrated in a single contact)

To do
To Do: property

  • Read the chapters 6, 9, 10-11 of Hanneman & Ridle on network techniques

  • Download/install Ucinet and the talk.dl data

  • Try it out!

  • (Install SPSS and fresh up your SPSS knowledge!)