Social Networks, Individuals and Small Worlds
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Social Networks of Individuals and Small Worlds
Dr Remko HelmsDept of Information and Computing Science
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?
1
2
3
4
5
Pat
1
23
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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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.