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Social Networks of Individuals and Small Worlds

Dr Remko HelmsDept of Information and Computing Science

@remhelmsr.w.helms@uu.nl

remhelms.wordpress.com

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Are you on social networks?

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Ontology of the lecture

Social Network Analysis

Knowledge Network Analysis

Social Sciences

Graph theory

SNA analysis techniques Social

network theories

Strong tie/Weak tie

Structural holes

Small Worldnetworks

Social Capital Communities

of Practice

KNA analysis techniques

Theory on knowledge sharing

networks

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Knowledge Network Analysis research

What network structure facilitates a good knowledge flow?

How do these knowledge networks evolve over time? Relation between online and offline networks? What roles can be distinguished in knowledge

networks? What is the role of technology in these networks? …

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Spot the Thought Leader

Cou

rtesy

of P

rof M

. Huy

sman

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Finding Communities of Practice

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Knowledge drain because of retirement

Cou

rtesy

of P

rof M

. Huy

sman

Red: retire in 2 yearsYellow: retire in 3-4 yearsGreen: retire more than 4 years

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Let’s first explore this …

Social Network Analysis

Social Sciences

Graph theory

SNA analysis techniques Social

network theories

Strong tie/Weak tie

Structural holes

Small Worldnetworks

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Rise of Social Network perspective

Network perspective gained interest in Sociological domain

Traditionally, behavior of people was explained by studying personal or environmental variables

Network theorists claim that behavior is also to a large extent influenced by personal relations

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What is a Social Network

Social Network: “A set of actors that may have relationships with one another.” (Hanneman & Riddle)

Relations can be of any type: Friend of Works with Learns from Exchanges information with …

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Social Network Analysis

The study of the integrity and development of social networks by means of qualitative and quantitative analysis, providing explicit formal statements and mathematical measures

Qualitative: Sociogram Quantitative: Measures such as density and

centrality (based on graph theory)

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Some SNA terminology

Actor (node/vertice)

Relational tie (link/edge)

Dyad Tryad

(Sub)group

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Sociogram

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Matrix representation of network data

Actor 1 Actor 2 Actor 3 Actor 4

Actor 1 - 1 0 1

Actor 2 0 - 1 1

Actor 3 0 1 - 0

Actor 4 1 1 0 -

Labeling of relationships: 0 = absent; 1 = present

FROM

TO

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Circular presentation

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Random presentation

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Spring-ed presentation

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Data collection: Survey or Interviews

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Tools to support network analysis

Examples of tools:- NetMiner- UCInet- Gephi- NodeXL

[screenshots from Netminer 3]

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Tools to support network analysis

Examples of tools:- NetMiner- UCInet- Gephi- NodeXL

[screenshots from Netminer 3]

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Some simple demographics

Degree (in/out): is number of incoming and outgoing links

Shortest path: shortest distance between actors

Density: relations present / total possible relations

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Which network has the highest density?

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Power and Centrality a simple view

[Hanneman & Riddle]

Which actor A has more power? (left or right)

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Power and Centrality:a more complicated view

Who has more Power?

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2

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Pat

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4

5

Chris

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Centrality and power measures

Degree centrality (in/out): degree of actor / total number of present ties

Bonancich’s power: as previous but including second and further degree ties

Closeness centrality: measure for how close an actor is to all other actors in the networks

Betweenness centrality: measure for how often an actor is on the shortest path between two other actors

=> On network level there is a centralization index

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Structural holes (Burt)

Simple example based on Hanneman & Riddle

I no structural hole II with structural hole

Advantageous position of person A based on his embeddedness in the network

Person crossing a structural hole is called a bridge

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Weak tie theory (Granovetter)Relational embeddedness

Insight that not every relationship is strong Amongst friends or direct colleagues: strong ties Acquaintances or distant colleagues: weak ties

Weak ties are typically bridges Strong ties for team work; weak ties for innovation

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LegendNode color: Location of actorOffice A: Office B: Office C:

Node size: Expertise of actorTrainee: Specialist: Expert:

Node shape: Knowledge role of actorCreator: ■ Sharer: ● User: ▲

■ ■ ■■ ■

LegendNode color: Location of actorOffice A: Office B: Office C:

Node size: Expertise of actorTrainee: Specialist: Expert:

Node shape: Knowledge role of actorCreator: ■ Sharer: ● User: ▲

■ ■ ■■ ■

Clustering

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Why do people cluster?Proximity and homophily

People cluster because they tend to make connections with other people according particular ‘rules’

Proximity If people are on the same location they meet and make

connections (Allen, 1979)

Homophily People with similar characteristics feel attracted, eg. same

education/school, same interest, same village/country, same function, same age

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6 degrees of separation

Everyone in the world knows each other through 6 other people… or in 6 degrees

Experiment by Milgram in the 60’s

Insight: networks are not randomly connected

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Small world phenomenon

Network with a few well connected HUBS that hold the network together Check your LinkedIn connections for the 500+

connections The average number of connections follows a power

law

Many ‘natural’ networks are organized as so-called ‘small worlds’. E.g. social relations between people but also the Internet

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Network patterns: single hub (a), multiple hub (b), random (c)

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Network patterns: Core / periphery

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Now explore the applicationof SNA in KM

Knowledge Network Analysis

Social Capital Communities

of Practice

KNA analysis techniques

Theory on knowledge sharing

networks

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Formal organization vs. informal organization

[Cross, Parker, Prusak, Borgatti, 2001][Brown & Duguid, 1991 ]

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Dimensions of Social Capital(Nahapiet & Ghosal, 1998)

Relational• kind of personal relationships

Structural• (impersonal) properties of

the network

Cognitive• resources providing shared

context

• Access to resources (network ties)• Network configuration

• Trust, Identification• Norms• Obligation (reciprocity) and expectations

• Common language• Shared stories• Narratives

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Social Capital and KM

Knowledge exchange is facilitated when:

1. There are structural links or connections between individuals (structural)

2. Individuals have the cognitive ability to understand and apply knowledge (cognitive)

3. Their relations have strong, positive characteristics (relational)

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Analysis of a Learning Network

Master-apprenticeship relationships Do experts transfer knowledge to trainees Do trainees receive knowledge from experts

Sub-communities Homophily and geographic location can be barriers for

knowledge exchange

Knowledge drain and knowledge brokers Experts leaving the organization Influential people leaving the organization Brokers leaving the organization

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Bottleneck analysisLack of learning relations

Clustering based on Girvan Newman algorithm

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Research question

How are structural characteristics of learning networks related to (Knowledge related Work) Performance?

Goal: Structural characteristics that match with high performance gives insight in ‘best’ structure for knowledge sharing, i.e. reference structure

NetworkStructure Performance

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Research Sample

Unit4 Agresso, a Dutch product software developer

10 European countries, US and Canada (foothold in AU)

In 2006 approx 2.700 employees

Research conducted at Wholesales business line with 99 employees (response rate 80%)

18 learning networks for different topics such as EDI, Procurement and Project Management

Participation in networks: avg. 5.7, s.d. 3.3

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Research model

Connectedness

Performance

Density

Reciprocity

Centralizationindex

Efficiency

Transitivity

H1+

H1a-

H2a-

H2b+

H3a+

H3b-

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Conclusions

Isolates and isolated subgroups should be avoided to stimulate free transfer of tacit knowledge in the knowledge network (connectedness)

More learning relations are not necessarily better; employees should be selective in their relations and avoid F-of-a-F relationships (density, efficiency, transitivity)

Instead of learning relations between expert and novices also learning relations between experts and experts, and novices and novices should be taken into account (centralization index)

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Problem definition

We typically tend to recognize a successful online community as we see one. E.g. iPhone forum in NL World of War Craft forum Health forums

But how do communities evolve over time and become successful?

What is the role of the interaction between the newcomers and the ‘oldies’ in becoming successful?

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Hallo! by the ‘Kamer van Koophandel’

Hallo! community for Dutch entrepreneurs Owned by the Dutch Chamber of Commerce Help, support and discussion forum

Taxes, tips & tricks on start-ups and experiences

8,00080%

20% 18 users (0.002%)

55,000 posts35,000 users

Not uncommon ... Twitter: 50% of all the following attentionis directed to 20,000 (0.05%) of users

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Edge-ratio analysis2009_1

Most active sub-category (306)

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Edge-ratio analysis2009_2

Most active sub-category (306)

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Edge-ratio analysis2009_3

Most active sub-category (306)

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Edge-ratio analysis2009_4

Most active sub-category (306)

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Edge-ratio analysis2010_1

Most active sub-category (306)

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Edge-ratio analysis2010_2

Most active sub-category (306)

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Edge-ratio analysis2010_3

Most active sub-category (306)

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Some interesting edge-ratio trends

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20%

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120%

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post/useredges between newcomers %edges between oldies %edges between old and new %newcomer node/edge ratio

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Publications on this topic

Reijsen, J. V., & Helms, R. (2009). Revealing knowledge networks from computer mediated communication in organizations. In S. Newell, E. Whitley, J. Wareham, & L. Mathiassen (Eds.), Proceedings of 17th European Conference on Information Systems (ECIS2009), Verona, Italy, pp. 2503-2515.

Helms, R., & Reijssen, J. V. (2008). Impact of Knowledge Network Structure on Group Performance of Knowledge Workers in a Product Software Company. In D. Harorimana & D. Watkins (Eds.), Proceedings of the 9th European Conference on Knowledge Management (ECKM2008), Southhampton, UK, pp. 289-296.

Helms, R. (2007). Redesigning Communities of Practice using Knowledge Network Analysis. In: A.S. Kazi & L. Wohlfart & P. Wolf (Eds.), Hands-On Knowledge Co-Creation and Sharing: Practical Methods and Techniques, Knowledgeboard, pp. 251-274.

Helms, R., & Buysrogge, C. (2006). Application of Knowledge Network Analysis to identify knowledge sharing bottlenecks at an engineering firm. In J. Ljungberg & M. Andersson (eds), Proceedings of the Fourteenth European Conference on Information Systems (ECIS2006), Göteborg, Sweden, pp. 1877-1889.