introducing network analysis as a research technique · hand-drawn sociogram. znetwork analysis is:...

Introducing Network Analysisas a Research Technique

by

Adjunct Associate Professor Graham Durant-Law CSC, PhDBSc, MHA, MKM, Grad Dip Def, Grad Dip Mngt, Grad Cert Hlth Fin, psc.

Copyright © 2013 – Graham Durant-Law

Ablutions Fire escape Seminar organised as four sessions

– Session 1: Background– Session 2: Visualising Networks – Session 3: Data– Session 4: Pitfalls (Iranian Nuclear Physicists)

Break between each session End time - 12:30 Chatham House rule Do

– ask questions– challenge– participate

Copyright © 2013 – Graham Durant-Law 2

In seeking wisdom, the first step is silence, the second is listening, the third remembering, the fourth practicing,

the fifth – teaching others.

— Solomon ibn Gabirol

Seminar Administration

Session One - Background

‘Each of us is part of a large cluster, the worldwide social net, from which no one is left out. We do not know everyone on this globe, but it is guaranteed that there is a path between any two of us in this web of people’.

Albert-Laszlo Barabasi, Physicist, 2002


What is network analysis?

What is the history of network analysis?

Where does network analysis fit in the research traditions?

Why is network analysis useful?

Why is network analysis gaining traction now?

Where is network analysis being used?

What is the lexicon of network analysis?


Session One - Background

5

Hierarchical Thinking

Everyone understands the hierarchical view, but this view does not allow for cross-branch communication


6

Network Thinking

This network view is exactly the same as the hierarchical view. This view could allow for cross-branch communication



Network Thinking

The thinking shift allows us to do this. This view does allow for cross-branch communication. Note what is different.

Network analysis is based on an assumption of the importance of relationships among interacting nodes.

A methodology that provides the ability to examine quantitatively, qualitatively, and graphically macro and micro linkages between nodes.

A connection between two or more nodes means there is some sort of relationship.

Unit of data is the dyad – pairs of nodes.


What is Network Analysis?


What is the History of Network Analysis?

Jacob Levy Moreno

Königsberg Bridge Problem (Leonhard Euler 1735)

Hand-drawn sociogram

Network analysis is:– sympathetic with systems theory and

complexity theory;– molecular rather than atomistic, stressing inter-

dependence;– guided by formal theory organised in

mathematical terms;– grounded in the systematic analysis of

empirical data.

Relational ties are primary. Attributes of nodes are secondary.

Relational ties are channels for transfer.

Different types of relations identify different networks even when imposed on identical nodes. e.g. trade exports vs. drug trafficking.


Where Does Network Analysis Fit in the Research Traditions?

Network analysis can be positioned in the constructivist tradition qualitative research paradigm, based on the systems thinking school.

In this school phenomena, such as knowledge, are thought of as being complex wholes of material and immaterial things, with the component entities being hierarchical, but of themselves being able to be treated as wholes.

Social phenomena are constructed and as such must be positioned in time, space and culture, but can be decomposed to smaller components. Furthermore the properties of these phenomena are emergent depending on where the system boundary is drawn.


What is the Philosophical Posture of Network Analysis?

Ludwig von Bertalanffy


Method or Methodology?

Methodology+ =Philosophy

Methods(procedures, tools and techniques)


Why is Network Analysis Useful?

Explores participants’ world-views and contrasts them with the debate in the literature – that is the methodology is participant centred.

Allows the researcher to develop a theory from the data that is ultimately acceptable to a wider audience - that is the methodology is researcher centred.


Why is Network Analysis Gaining Traction Now?

Christakis, N & Fowler, J, 2007, ‘The Spread of Obesity in a Large Social Network over 32 Years’, New England Journal of Medicine, Vol 357, pp. 370-379.

organisational studies knowledge management project management economics epidemiology kinship studies political science mental health risk studies homeland defence terrorism and more!


Where is Network Analysis Being Used?

Copyright © 2013: Graham Durant-Law 16

Communication in Practice Pentland A, ‘The New Science of Building Great Teams’, Harvard Business Review, April 2013

A 2011 study of 2,500 participants by the Massachusetts Institute of Technology found that the most important predictor of team success is in its communication patterns.

Of note the study found that:– communication patterns are as significant as all

other factors, including intelligence, personality, and talent combined;

– researchers could foretell which teams would out-perform the others simply by looking at the data on their communication patterns, even without meeting the team members;

– connectivity, activity, and energy were the key communication dynamics that enabled or effected performance;

– mapping communication behaviours over time, and making small adjustments to move it closer to the ideal, dramatically improves team performance.

1ij ji

ij

r A Am


What is the Lexicon of Network Analysis?

A node is the smallest unit in the network. It is also known as a vertex.

A tie is a line between two nodes indicating there is a relationship between them.

A graph is a set of nodes and a set of ties between pairs of nodes.

A network consists of a graph and additional information on the nodes or the ties of the graph. It is also known as a map.


Measures

Ties (links): in ties and out ties represent the number of connections to and from a node.

Density: the percentage of connections that exist out of the total possible that could exist.

Distance: degrees of separation or the diameter of a network.

Reciprocity: the number of bi-directional links expressed as a percentage.

Centrality: the extent to which a network is organised around one or more central nodes.


Centrality

Centrality is the degree to which an actor occupies a central position in the network in one of the following ways:

– having many ties to other actors (degree centrality)

– being able to reach many other actors (closeness centrality)

– connecting other actors who have no direct connections (betweenness centrality)

– having connections to centrally located actors (eigenvector centrality)

Kilduff, M & Tsai, W 2005, Social networks and organisations, Sage Publications, London, p 132


Sizing by Degree Centrality(an activity measure)

Reveals how much activity is going on and who are the most active members by counting the number of direct links each person has to others in the network.

Does not necessarily describe power or influence.

People at the centre of the network:• are the connector or hub of

the network,• may be in an advantaged

position in the network.• are usually less dependent

on other individuals.• are often a deal maker or

broker.

Commentators(receivers and transmitters) - degree centrality

1

n

i ijj

k A

Where ki is the degree of node i; n is the number of nodes; Aij is an adjacency matrix; and ijdenotes a tie between nodes iand j.

1

nini ij

j

k A

In-degree is the number of ties directed towards the node.

1

noutj ij

i

k A

Out-degree is the number of outgoing ties from the node.


Sizing by Closeness Centrality(a proximity measure)

Highlights people with the shortest paths to other people, thus allowing them to directly pass on and receive communications quicker than others in the organisation.

Is strongly correlated with organisational influence if the individual is a skilled communicator.

These individuals are often network brokers. They are often the ‘pulse-takers’ of the organisation.

Conduits(providers and seekers) - closeness centrality

1i ij

j

l dn

Where li is the mean distance; n is the total number of nodes; and dij is the length of the shortest path between nodes iand j in a matrix.

• Closeness centrality begins with the assumption that having short paths to other nodes increases the influence in the network of that node.

• It measures the average distance a node is from all other nodes in a network, and therefore is a proximity measure.

• Unconnected nodes by definition have an infinite distance between them, which means scores cannot be computed for isolated nodes.

• Closeness centrality requires the network, or at least the component under examination, to be complete.


Sizing by Betweenness Centrality(a position measure)

Reveals individuals who:• connect disparate groups

within the network.• hold a favoured or

powerful position in the network.

• have great influence over what is communicated through the network.

• act as intermediaries

Identifies the bridges within the network. They may act as the true gatekeeper deciding what does or does not get passed through the network, or as the “third who benefits” by passing information to others to secure advantage..

Controllers(brokers and gatekeepers) - betweenness centrality

ististst

nxg

Where xi is the betweenness of node i; is the number of paths from node s to node t that pass through node i; and gst is the number of paths from node s to node t.

• Betweenness centrality measures the extent that a node lays on the path of other nodes.

• Betweenness centrality is unlike other centrality measures because it does not measure how well the node in question is connected, but rather how it connects components of the network.

• It is a proxy for understanding strategic position within the network.

• It can be applied to both directed and undirected networks.


Sizing by Eigenvector Centrality(an advantage measure)

Measures how well connected a person is and how much direct influence they may have over the most active people in the network

Measures how close a person is to other highly connected people in terms of the global or overall makeup of the network

Is a reasonable measure of “network positional advantage” and/or perceived power.

Connectorseigenvector centrality

11i ij j

j

x k A x Where xi is the centrality of each node i; k is the eigenvalue, with 1 being the largest and -1 the smallest; Aij is an adjacency matrix; and ij denotes a tie between nodes i and j.

• Eigenvector centrality begins with the assumption that having connections with other central nodes increases the relative importance of that node.

• A high eigenvector centrality score means the node is important because either it is connected to many nodes, or is connected to a few very highly connected nodes

• Eigenvector centrality has the limitation that it works best on undirected networks.


Network Laws in Social SituationsCross, R, Parker, A & Sasson, L (eds) 2003, Networks in the knowledge economy,

Oxford University Press, New York.

• Law of Emergence - Relationships are unimpeded by pre-ordained formal structures.

• Law of Propinquity - Those close by form a tie. The probability of two people communicating is inversely proportional by a factor of 2 to the distance between them.

• Law of Oligarchy - Birds of a feather flock together. Social strata fulfilling particular functions tend to become isolated over time.

• Law of Links - The number of possible links in a social system = N(N-1) or sometimes N(N-1)/2. 152 nodes = 22,952 links!


Dunbar’s Numbers.Dunbar, R 2010, How many friends does one person need? Dunbar's number and other evolutionary quirks.,

Faber and Faber, London.

Dunbar’s Numbers are an indicator of meaningful relationships and the maximum effective number of people in a network. The usually accepted number is 152. There is an mega-band number of around 700, and an upper limit of about 1,500.

650

270

127

85

35

18

10

5Intimate

Immediate

Close

Extended

Acquaintances

IncreasingIntimacy

IncreasingConnections

15

33

68

152

338

708

1,448

5

Session Two – Visualising Networks

‘Simplicity is the key to effective scientific inquiry.’Stanley Milgram, Sociologist, 1973



Session Two – Visualising Networks

What are the different types of network analysis?

What visualisation and analysis tools are available?

What visualisations are available in the tools?

What are the strengths and weaknesses of the tools?


What are the Different Types of Network Analysis? What is SNA?

Social Network Analysis is a research methodology that focuses on relationships between and among social entities, and on the patterns and implications of these relationships.

It is focused on uncovering the patterns of people's inter-connectedness and interactions.

There usually is no researcher intervention in the network.


What are the Different Types of Network Analysis? What is ONA?

Organisational Network Analysis is a research methodology and business technique that uses SNA methods to examine the social arrangements of organisations.

The unit of interest is the group or organisation.

There may be researcher or organisation intervention in the networks. (action research)


What are the Different Types of Network Analysis? What is BNA?

Business network analysis is a diagnostic methodology that elicits the capacity of an organisation to effectively engage in its activities.

BNA uses both SNA and ONA methods, but extends the analysis beyond people or organisations to include, amongst other things, data flows and policy relationships.

BNA also employs link analysis and system dynamics techniques.

Almost always followed up with interventions. (action research)


What Visualisation and Analysis Tools are Available?

EXCEL 2007/2010 and NodeXL UCINET and NetDraw NetMiner III and NetMiner IV NetMap Analytics Sentinel Visualizer PAJEK Mage Condor ENet and KeyPlayer Visone Inflow StocNET Gephi Pnet R, ggplot2, and RSiena and many more

http://www.insna.org/INSNA/soft_inf.html


UCINET (NetDraw) Spring Diagram


NetMiner III & IV Spring Diagram


NetMiner III & IV Spring Diagram Group Analysis (Block-modelled)


NetMiner III & IV Wheel Diagram Eigenvector Centrality


Sentinel Visualizer Circles Layout

ExchangeAgreement

Unspecified Agreement

ArticulationAgreement

Research CollaborationAgreement

Study AbroadAgreement

Transnational EducationAgreement

= UC


Sentinel Visualizer Step Analysis


NetMap Analytics Step Analysis


NetMap Analytics Step Analysis (Filtered)


PAJEK


EXCEL 2007 and NodeXL – Spiral Map


EXCEL 2007 and NodeXLSine Wave Map


What are the Strengths and Weaknesses of the Tools?

Colours and layers are more easily applied in NetDraw and NetMiner.

Spring diagrams are easier to layout in NetDraw.

NetMap Analytics and Sentinel Visualizer are very good at Step Analysis.

UCINET is good for analysis, but the understanding of the measures is assumed.

NetMiner brings analysis and visualisation together.

PAJEK, NetMap Analytics, and NetMiner are best at handling very large datasets.

EXCEL and NodeXL are emerging as simple and powerful tools.

Session Three - Data

‘Science is built with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house’.

Henri Poincare, Mathematician, 1901.



Session Three - Data

How are data collected and structured?

What is a one-mode network?

What is a two-mode network?

What tools can I use for data collection?

How are data imported into UCINET and NetDraw?


How are Data Collected and Structured? The Generic Process

Determine the unit of analysis. This is arguably the most important step, as it determines how data is collected and which tools and analysis techniques should be employed.

Determine the questions. The questions depend on the unit of analysis, and what you want to discover.

Collect the data. Typically the questions are answered using a survey. The survey can be done in person, on paper, or be web-enabled. Where appropriate data collection can also be done using data-mining techniques. For example intra-departmental e-mail traffic could be mined. In the case of a policy relationship mapping exercise the documents are parsed for key words, headings and other relevant attributes.

Import the data into a visualisation tool. Typically data is entered into a Microsoft EXCEL workbook or database, and then imported into a visualisation tool.


How are Data Collected and Structured? A More Specific Process


How are Data Collected and Structured? A Mind Map


How are Data Collected and Structured? Sample Questions

Type Example QuestionsOrganisational Interface Mapping

Please identify up to 10 people who work in external departments and who are important to you in your professional network. These can be people who provide you with information to do your work, help you think about complex problems posed by your work, or provide developmental advice or personal support helpful in your day-to-day working life. These may or may not be people you communicate with on a regular basis and must come from an organisation external to yours.

Project Interface Mapping

Please identify the people in other project teams that you rely on to provide information for your project. For each person you have identified please assign a score based on the amount of contact you have with them. 1 is the most amount of contact. 10 is the least amount of contact. Each score should be different.

Information Flow Mapping

Please identify the people in your department you have passed documents or e-mails to in the last month. These may or may not be people you communicate with on a regular basis, but they must be part of your department.

Collaboration Mapping

Please identify the people who are important to you in your professional network. These can be people who provide you with information to do your work, help you think about complex problems posed by your work, or provide developmental advice or personal support helpful in your day-to-day working life. These may or may not be people you communicate with on a regular basis and must come from within your organisation.

Social Capital Mapping

In your workplace who do you go to for information that helps you solve problems or capitalise on opportunities?

50

Homogenous Networks

Also known as a one-mode or adjacency network.

We link like nodes with like nodes. For example:

– people to people– teams to teams– projects to projects

Almost all network measures can be used, and visualisations can be easily layered.

Data can be captured and structured as a square matrix.



What is a One-Mode Network?

John Thomas Anna James Peter Mary Michael David Anthony Bobby

John 0 0 0 0 0 0 0 1 0 0

Thomas 0 0 0 0 0 0 1 2 0 0

Anna 0 0 0 0 0 0 0 0 0 0

James 0 0 0 0 0 0 0 0 0 0

Peter 0 0 0 0 0 0 0 0 0 0

Mary 0 0 0 0 2 0 0 0 0 1

Michael 0 0 0 2 0 0 0 0 0 0

David 0 2 0 0 0 0 0 0 0 0

Anthony 0 0 0 0 0 0 0 0 0 0

Bobby 0 0 0 0 4 1 0 0 0 0


What is a One-Mode Network?

53

Heterogeneous Networks

Also known as a two-mode or affiliation network.

We link two types of nodes with each other. For example:

– people to projects, or– people to teams.

The relationship between like nodes is through the other node. For example the University of Canberra is linked to Harvard University through (or by) a Research Collaboration Agreement.

Data is captured and structured as a rectangular matrix or edge-list.



What is a Two-Mode Network?

ID Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10

CY10034 0 0 0 1 1 0 0 0 0 1

CY10039 1 0 0 1 0 0 0 0 0 0

CY10044 0 0 0 1 1 0 0 0 0 0

CY10045 0 0 1 0 0 0 0 0 0 0

CY10047 0 0 1 0 0 0 0 0 0 0

CY10054 1 0 0 1 0 0 0 0 0 0

CY10055 0 1 0 0 1 0 1 0 0 0

CY10057 0 1 0 0 0 0 0 0 0 0

CY10059 0 1 0 0 0 0 0 0 0 0


What is a Two-Mode Network?

56

Matrix versus Edge-List

Matrix Format

FE 1 FE 2 FE 3 FE 4

FE 1 0 1 1 1

FE 2 0 0 1 1

FE 3 1 0 0 0

FE 4 1 0 1 0

Edge List Format

From To

FE 1 FE 2

FE 1 FE 3

FE 1 FE 4

FE 2 FE 3

FE 2 FE 4

FE 3 FE 1

FE 4 FE 1

FE 4 FE 3


57

Compound Heterogeneous Networks

The realm of link analysis.

We link three or more types of nodes with each other. For example, people to teams to projects to teams.

Again the relationship between like nodes is through the other nodes.

Generally we are looking at links between unlike nodes

Many network measures cannot be reliably used.

Data is captured and structured as a rectangular matrix or edge-list.


58

Do you Understand the Question Context?



Data Collection – Paper Survey


Data Collection - EXCEL


Data Collection – ONA Surveys


Data Collection – Lime Surveys


How are Data Imported into UCINET and NetDraw?


But Many Networks Look Like This

Which of the aforementioned centrality measures can you use on this network?

Attributing Data Using Behaviour (B is the person).

Wassermann, S & Faust, K 1999, Social network analysis, Cambridge University Press, Cambridge.

66

Isolate - a person that has no links. A B C

A B C

A B C

A B CReceiver - a person that has only in-links.

Transmitter - a person that has only out-links and no in-links.

Carrier - a person that has an equal number of in-links and out-links.

Other - a person that does not fall into the previous categories.

A B C



Attributing Data Using Roles

A B C

A CB

A CB

A B C

Gatekeeper - a person who transmits information and other resources to the same group or team from sources external to that group or team.

Representative - a person who transmits information and other resources from their group or team to an external group or team.

Liaison - a person who transmits information and other resources from one group or team to another group or team, whilst themselves belonging to a different group or team.

Coordinator - a person who brokers connections within the same group or team.

A CBConsultant - a person who intermittently takes the central lead by connecting others in the same group or team, but who belongs to another group or team.

68

Allows us to do this ...

Information Network > weekly

Brokerage

Dynamic

This example is an evaluation of a $70 million NSW project-program involving multiple government agencies. Note the communication patterns of the highlighted agencies. The light-blue agency circled in orange is the communication lynchpin and is under-resourced.



Applying a Metric

If an individual only sends messages and receives none then their contribution index is +1.000If an individual only receives messages and sends none then their contribution index is -1.000If the communication behaviour is balanced then the contribution index is 0.000

ContributionFrequency

ContributionIndex

Sender +1

Receiver -1

Expert

Envoi

Escort

Expediter

Gloor, P 2006, Swarm creativity: Competitive advantage through collaborative innovation networks, Oxford University Press, Oxford.

messages sent – messages received

messages sent + messages receivedContribution Index =


Changes the network analysis from this …


To this …http://www.tandfonline.com/eprint/guxW2FC2CfYCEGt5yPkh/full

No Discernible Role

Expert

EscortEnvoi

Expediter

1. The links inside the “circles” are posts between like roles. Note there are no posts between Experts.2. The thicker curves linking groups are consolidated exchanges between groups. They do not show frequency, or links from one

individual to another.3. Note the relative density in the Escort and Expediter groups.

Or even this …



And in turn allows deeper analysis like this …

Escort and Expediter Network Sized for Betweenness (Controllers)

Larger nodes have greater betweenness within their group, and therefore a better strategic

position within the network.


And greater understanding like this …

Escort and Expediter Network Consultant Brokerage

A CB

Larger nodes have greater betweenness within their group, and therefore a better strategic position

within the network, but note who holds the consultant roles.

Session Four - PitfallsIranian Nuclear Physicists Example

‘Simply because your data links people and you can visualize that, it does not mean you have performed network analysis. This is akin to displaying a line plot of some stock's price over a quarter and claiming you have performed statistical analysis – all you have done is report data! As with all other statistical processes, network analysis is meant to draw meaning and inference from the structure, which requires an understanding of these methodologies, their strengths and limitations’.

Drew Conway, Political Scientist, 2009.



The Network? UCINET/NetDraw(network analysis nirvana?)


Cut-Points(people holding the network together)


Nodes Sized by Degree(in and out links)


Nodes Sized by Betweenness(ability to get to others)


The Same Network? NetMiner III (closer to network analysis nirvana)


Discrete Network


Degree Centrality


Betweenness Centrality


Sattari - Ego Network

20042009

Seminar Concluding Remarks

‘The most incomprehensible thing about the world is that it is at all comprehensible’.

Albert Einstein, Physicist, 1921.



What are the Ethical Considerations of Using Network Analysis?

Ethics in network analysis can be a vexed question.

Non-response does not immediately guarantee omission from the study.

It is very easy for data, and the visualisations to be used in unintended ways and/or to be misinterpreted.

Must read ‘Toward ethical guidelines for network research in organizations’by Professor Steve Borgatti.

http://www.analytictech.com/borgatti/papers/ethics2005.pdf


How Should a Network Analysis be Presented?

To remove ambiguity always include the question on the slide.

Remove node labels unless they are necessary for understanding.

Try to space the diagram to minimise link crossovers.

Where appropriate include arrowheads to show direction.

Do use colour, size and shapes.

Do filter data to show only what is necessary for understanding.

Always remember there are multiple ways to present data, and hence multiple interpretations.


Consider Other Ways to Present Data

Branch Person Level Process Influence

Outcome Interest

CurrentRating Action Behaviour

1 Fraser Same 3 3 9 Engage

1 Gunn Higher 5 4 (-) 20 (-) Manage

2 Logan Higher 5 5 (+) 25 (+) Manage

2 McNab Same 3 5 (+) 15 (+) Inform

2 Munro Same 3 4 (-) 12 (-) Inform

3 McLeod Lower 1 5 (+) 5 (+) Monitor


Some Issues

Must clearly define the ‘unit of analysis’ – that is what are nodes, what are ties, and what are attributes.

Must define the population, and then cover the whole population to get meaningful network statistics.

Requires specialist software.

Usual limitations of survey techniques.

Few research exemplars.


Journals


Cautions

Mathematical approaches to network analysis tend to treat the data as ‘deterministic’. That is, measurements are viewed as an accurate reflection of the ‘real’ or ‘final’ or ‘equilibrium’ state of the network.

Observations are usually regarded as the population of interest rather than a sample of some larger population of possible observations.

You must understand your organisation, the data, the resultant network and the assumptions you are making!

Hanneman, R & Riddle, M 2005, Introduction to social network methods,http://faculty.ucr.edu/~hanneman/


Other seminars and workshops


Web Resources

Connections - The official journal of INSNA. It has full text articles back to 1977. http://www.insna.org/indexConnect.html

International Network for Social Network Analysis http://www.insna.org/

Hanneman, R & Riddle, M 2005, Introduction to social network methods, http://faculty.ucr.edu/~hanneman/

Vancouver Network Analysis Team http://www.sfu.ca/~richards/

Ronald Burt's homepage http://gsb.uchicago.edu/fac/ronald.burt

Visible Path – the web pages and blog of Stanley Wassermann http://www.centralityjournal.com/

Social Networks Laboratory The University of Melbourne. http://www.psych.unimelb.edu.au/research/labs/UHSN_lab.html

Virtual Observatory for the Study of Online Networks - Australian National University http://voson.anu.edu.au/


Essential Reading

Barabasi, AL 2003, Linked. How everything is connected to everything else and what it means for business, science, and everyday life, Plume, New York.

Breiger, R, Carley, K & Pattison, P (eds) 2001, Dynamic social network modeling and analysis, The National Academies Press, Washington.

Buchanan, M 2002, Nexus. Small worlds and the groundbreaking theory of networks, W.W. Norton & Company, New York.

Cross, R & Parker, A 2004, The hidden power of social networks, Harvard Business School Publishing, Boston.

Durlan, MM & Fredericks, KA (eds) 2006, Social network analysis in program evaluation, Wiley, Minnesota.

Scott, J 2005, Social network analysis: a handbook, 2 edn, Sage Publications, London.

Surowiecki, J 2004, The wisdom of crowds. Why the many are smarter than the few, Abacus, London.

Watts, DJ 2003, Six degrees: the science of a connected age, W.W. Norton & Company, New York.


More Advanced Reading

Carrington, P, Scott, J & Wassermann, S (eds) 2005, Models and methods in social network analysis, Cambridge University Press, Cambridge.

Cross, R, Parker, A & Sasson, L (eds) 2003, Networks in the knowledge economy, Oxford University Press, New York.

de Nooy, W, Mrvar, A & Batagelj 2005, Exploratory social network analysis with PAJEK, Cambridge University Press, New York.

Freeman, LC 2004, The development of social network analysis. A study in the sociology of science, Empirical Press, Vancouver.

Hanneman, R & Riddle, M 2005, Introduction to social network methods, http://faculty.ucr.edu/~hanneman/

Hill, R & Dunbar, R 2002, ‘Social Network Size in Humans’ in Human Nature, Vol. 14, No. 1, pp. 53-72.

Kilduff, M & Tsai, W 2005, Social networks and organisations, Sage Publications, London.

Wassermann, S & Faust, K 1999, Social network analysis, Cambridge University Press, Cambridge.

Seminar Questions?

‘The real questions refuse to be placated… They are the questions asked most frequently and answered most inadequately, the ones that reveal their true natures slowly, reluctantly, most often against your will’.

Ingrid Bengis, Author, 1973


introducing network analysis as a research technique · hand-drawn sociogram. znetwork analysis is:...

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