km4dev discussion group social network analysis 2012 prepared by:a/prof graham durant law csc, phd...

KM4DEV Discussion GroupSocial Network Analysis 2012

Prepared by: A/Prof Graham Durant Law CSC, PhDCorrect at: 6:00 am AEST on the 24th of June 2012

http://www.hyperedge.com.au/

Copyright © 2012: HyperEdge Pty Ltd 2

Caveat Emptor

The diagrams presented in this document are derived from just over 11 years of data provided by KM4Dev in an XML format.

Data was dirty and required substantial cleaning to remove identified pseudonyms, aliases, and duplicate names, as well as non-printing characters. HyperEdge cannot guarantee that all pseudonyms and aliases have been removed.

"Anonymous" posted about 18% of all posts. It was necessary to remove "Anonymous" from the dataset because HyperEdge could not be sure "Anonymous" was a single person.

Removing identified pseudonyms, aliases, and duplicate names, and "Anonymous" leaves 703 identified individuals in the network. Collectively, these people comprise the node-set for the public bounded or contained network, for which activity and various network measures have been applied.


Caveat Emptor (continued)

There is no way to measure the private interactions or shadow network from the data supplied.

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. Clearly this not the case as the network is constantly changing and evolving. Local knowledge should therefore be applied to the interpretation of data.

The diagrams presented in this document are drawn from filtered data. Data can be filtered in multiple ways resulting in differing diagrams. It is also possible to present other forms of diagrams from the same filtered data.

Occasionally the results appear contradictory. Local knowledge should therefore be applied to the interpretation of the maps.

4

Mapping Objectives

HyperEdge was asked to conduct a social network analysis in order to allow the KM4Dev Core Group, and the wider community, to:

– better understand KM4Dev in terms of issues such as identity, relationships, function and role;

– better understand the relationship of SIWA and SA-Ge to each other and the main community;

– better understand where KM4Dev sits in comparison with other networks to help KM4Dev appreciate where they are unique, where they are in terms of a traditional life-cycle, and how they might evolve; and

– implement practical methods to support the growth and enrichment of the KM4Dev community.

Copyright © 2012 – HyperEdge Pty Ltd

Copyright © 2012: HyperEdge Pty Ltd

A Quick Social Network Analysis Lesson

5


Copyright © 2012 – HyperEdge Pty Ltd 6

Typical 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.

Organisational Dynamics


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.

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.

Providers and Seekersdegree centrality

Transmitters and Receiverscloseness centrality

Organisational Dynamics (continued)


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..

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.

Bridges (brokers and gatekeepers) - betweenness centrality

Influencerseigenvector centrality

Brokerage Roles (B is the Broker).


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.


Some Basic Activity Statistics

10



Total Posts per Annum (inclusive of “Anonymous” and self-replies)

Note: 2000 and 2012 are incomplete years


Total Posts per Annum (exclusive of “Anonymous” and self-replies)

Note: 2000 and 2012 are incomplete years


Total Posts by Month (inclusive of “Anonymous” and self-replies)


Total Posts by Month (exclusive of “Anonymous” and self-replies)


Total Posts by Day (inclusive of “Anonymous” and self-replies)


Total Posts by Day (exclusive of “Anonymous” and self-replies)


Engagement Pattern - Reciprocated Posts(exclusive of “Anonymous” )

This measure means the link is reciprocal: that is, there is an arrowhead at both ends. It does not measure the weight of the link: that is, the number of reciprocated posts.


Participating People and Posts by Time(exclusive of “Anonymous”)

Dunbar’s Numbers are an indicator of meaningful relationships and the maximum effective number of people in a network. The usually accepted number is 153. There is an mega-band number of around 500, and an upper limit of about 1,500 – see Dunbar, R 2010, How many friends does one person need? Dunbar's number and other evolutionary quirks., Faber and Faber, London.


Unique Participants in the Network(Dunbar’s and Wellman’s Numbers)

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

Wellman, B 2011, 'Is Dunbar's Number up?', British Journal of Psychology, pp. 1-3.

Dunbar’s Number

Wellman’s Number


90-9-1 Community Participation Heuristic

A 2010 study by Dr Michael Wu, using ten years of data from more than 200 online communities, found that:

– 90% of all users are “lurkers” who don’t actively contribute.

– 9% of all users are “occasional contributors” providing less than 50% of the content.

– 1% of all users are “hyper-contributors” providing greater than 50% of the content.

Using this heuristic the predicted size of the KM4Dev discussion group is 2,420 people.

http://lithosphere.lithium.com/t5/Building-Community-the-Platform/The-90-9-1-Rule-in-Reality/ba-p/5463


Comments

Removing “Anonymous” and self-replies has not changed the basic pattern of network activity.

Reciprocal posts appear low given this is a “knowledge exchange” network. It is perhaps indicative of a “knowledge seeking” organisation rather than a “knowledge sharing” organisation.

For the previous four years the active or public network appears to have stabilised around Wellman’s number.

– The literature suggests growth beyond this number may be difficult, unless a number of smaller sub-groups or special interest groups are formed, or the group is actively nurtured.

The predicted current size of the KM4Dev Discussion Group is 2,420 people, comprising 2,178 lurkers, 218 occasional contributors, and 24 hyper-contributors.


The Growth of the Network

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1. All people who participated from 2001 to 2011 are in the network.

The Growth of the Network - 2001(exclusive of “Anonymous” and self-replies)


1. All people who participated from 2001 to 2011 are in the network2. This diagram does not include the previous years links.



Complete Network Metrics(exclusive of “Anonymous” and self-replies)

People 703

Unique Links 2,981

Reciprocated Links 1,770

Total Links 4,751

Maximum Geodesic Distance (Diameter) 8.000000

Average Geodesic Distance 2.931082

Graph Density 0.007325

1. Geodesic distance means theoretically everyone in the network can reach each other in a maximum of 8 steps. Most can reach everyone in three steps: that is, there are three degrees of separation in the network.

2. Graph density might be low given this is a “knowledge exchange” network and less than 1% of all possible links are present.


Comments

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. Clearly this not the case as the network is constantly changing and evolving.

Noting the caution above, the network appears to have stabilised and reached an equilibrium state, with more or less the same participants.

Given the number of people in the network and its obvious complexity, a way of attributing and partitioning the network for further analysis is necessary.

One way of attributing data might be to use posting frequency or posting patterns.

Data and visualisations from this section, coupled with the previous section, suggest the a logical data partition is the 1st of January 2008 to the 31st of December 2011.


Attributing the Complete Network

36



Gloor’s Contribution Index

messages sent – messages received

messages sent + messages received

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.


Frequency of Sending and Receiving(exclusive of “Anonymous” and self-replies)

High Posting Frequency >= 59 posts being two standard deviations from the meanMedium Posting Frequency >=12 but <59 posts being one standard deviation from the mean.Low Posting Frequency <12 posts.

Key Participant Groups


Frequency of Sending and Receiving(exclusive of “Anonymous” and self-replies)

1. Red circles are people with high frequency activity >= 59 posts being two standard deviations from the mean.

2. Blue squares are people with medium frequency activity >=12 but <59 posts being one standard deviation from the mean.

3. Green triangles are people with low frequency activity <12 posts.

4. Noting the list is incomplete because of space reasons, the most active people in order are:

a. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]. [email protected]


Role Types by Contribution Index(exclusive of “Anonymous” and self-replies)

No Role people are low frequency senders and receivers with a contribution index between -0.499 and +0.499.Experts are go to people, but are low frequency receivers with a contribution index between -0.500 and -1.000.Envois are low frequency senders with a contribution index between +0.500 and +1.000.Escorts are medium frequency senders and receivers, with a contribution index between -0.499 and +0.499.Expediters are high frequency senders, with a contribution index between 0.000 and 1.000.

Key Participant Groups



No Role Envoi Expert

[email protected] [email protected] [email protected]














1. The 14 people with the “highest” index for their role type have been selected. For the “No Role” people this means an index of -1, and for the “Expert” people it means an index of 0.

2. This list is incomplete because of space reasons. The complete list could be provided as an annex to the report.



Escort Expediter

[email protected] [email protected]














1. The 14 people with the “highest” index for their role type have been selected. For the “Escort” people this means an index of 0, and for the “Expert” people it means an index of 1.

2. This list is incomplete because of space reasons. The complete list could be provided as an annex to the report.


Engagement Pattern by Role Type(exclusive of “Anonymous” and self-replies)

No 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.


Engagement Pattern by Role Type(exclusive of “Anonymous” and self-replies)


Comments

There are about 116 “key” active members in the network. – These people are either Escorts or Expediters.– 116 falls within the range of a Dunbar number, with room for marginal

growth.

There are 21 Expediters. This is very close to Wu’s Heuristic prediction of 24 hyper- contributors.

Gloor’s index provides a useful way of attributing the network, and other potential way of partitioning it.

km4dev discussion group social network analysis 2012 prepared by:a/prof graham durant law csc, phd...

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