Social Networks, Individuals and Small Worlds


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Guest lecture at Free University in Knowledge Management course by Maura Soekijad and Roos Erkelens

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  • LinkedIn networks but same for Facebook, there is a Gephiplugin for that.
  • You might call this an ontology.
  • You might call this an ontology.
  • The set of actors form a social structure that can be thought of a network. These networks are the object of study of social network analysis.
  • Simple Circular algorithm used
  • Simple Random algorithm used
  • Springed algorithm used (in my opinion this provides the most intuitive picture
  • This and the next slides are measures of cohesion in groups.
  • Density is in al dezenetwerkengelijk. Plaatjelaatzien hoe makkelijk je op het verkeerde been wordtgezet.Er zit eenanimatie in het plaatjewaardoorzichtbaarwordtdat de density van alsdezenetwerkengelijk is.
  • Also power is related to Social Capital theoryThe left one is more powerful and what you actually use to determine this is Freeman’s degree centrality, which is counting the number of direct relationships. But there came critique to this formalization of centrality. This can be explained using the next slide ….
  • Structural hole links to social capital theory as well. A has more social capital than B and C.With structural holes it is about getting an advantageous position by being the first to know new things which can be an advantage. It is about avoiding redundancy in links of your contacts.
  • Also structural holes are considered to be a measure of embeddedness (see Wijk, Van den Bosch et al.)Weak ties more relate to relational embeddeness and structural holes to structural embeddedness.
  • Six Degrees is het ideedat door de interconnectedness van netwerken van menseniedereen op beperkteafstand van elkaarstaat. Het gaathieromnetwerkenzondereensocialestructuurzoalseenbepaaldegemeenteoid.Wikipedia: A Facebook platform application named “Six Degrees” has been developed by Karl Bunyan (London network), which calculates the degrees of separation between different people. It has about 4.5 million users (as of April 7, 2008), as seen from the group's page. The average separation for all users of the application is 5.73 degrees, whereas the maximum degree of separation is 12. The application has a "Search for Connections" window to input any name of a Facebook user, to which it then shows the chain of connections.
  • Small world phenomenon is explanation why people can be reached in only 6 steps, it is because the HUB structure of many ‘natural’ networks.
  • Picture to demonstrate that different network configurations can have different implications for knowledge sharing.Multiple hub structure is also referred to as small world networks and here the degree distribution follows a power law. This thinking is also the basis behind the six degrees of separation experiment of Milgram in het 60s.
  • You might call this an ontology.
  • In other words the social capital view became more importantfor organizations.
  • What does the Social Capital view add to the Knowledge Management field?Paper van Borgatti & Cross (2003) over relational view on information seeking is eengoedvoorbeeld van onderzoek op het gebied van de relational dimension.
  • Online Community = online forum
  • They should be communicating more with each other because that brings success....
  • Social Networks, Individuals and Small Worlds

    1. 1. Social Networks of Individuals and Small Worlds Dr Remko Helms Dept of Information and Computing Science @remhelms remhelms.wordpress.com1
    2. 2. Are you on social networks?2
    3. 3. Ontology of the lecture Graph Social theory Sciences Social Capital Communities of Practice Social Network Analysis Knowledge Network Analysis SNA analysis techniques Social network theories KNA analysis techniques Strong tie/ Theory on Small World knowledge sharing Weak tie networks networks Structural holes3
    4. 4. 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?  …4
    5. 5. 5 Spot the Thought Leader Courtesy of Prof M. Huysman
    6. 6. Finding Communities of Practice6
    7. 7. Knowledge drain because of retirement Courtesy of Prof M. Huysman Red: retire in 2 years Yellow: retire in 3-4 years7 Green: retire more than 4 years
    8. 8. Let’s first explore this … Graph Social theory Sciences Social Network Analysis SNA analysis techniques Social network theories Strong tie/ Small World Weak tie networks Structural holes8
    9. 9. 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 relations9
    10. 10. 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  …10
    11. 11. 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)11
    12. 12. Some SNA terminology  Actor (node/vertice)  Relational tie (link/edge)  Dyad Tryad  (Sub)group12
    13. 13. Sociogram13
    14. 14. Matrix representation of network data TO Actor 1 Actor 2 Actor 3 Actor 4 FROM Actor 1 - 1 0 1 Actor 2 0 - 1 1 Actor 3 0 1 - 0 Actor 4 1 1 0 -14 Labeling of relationships: 0 = absent; 1 = present
    15. 15. Circular presentation15
    16. 16. Random presentation16
    17. 17. Spring-ed presentation17
    18. 18. 18 [Example uses (formerly PhPSurveyor.or Data collection: Survey or Interviews18
    19. 19. Tools to support network analysis Examples of tools: - NetMiner - UCInet - Gephi - NodeXL19 [screenshots from Netminer 3]
    20. 20. Tools to support network analysis Examples of tools: - NetMiner - UCInet - Gephi - NodeXL20 [screenshots from Netminer 3]
    21. 21. Some simple demographics  Degree (in/out): is number of incoming and outgoing links  Shortest path: shortest distance between actors  Density: relations present / total possible relations21
    22. 22. 22
    23. 23. Which network has the highest density? 2323
    24. 24. Power and Centrality a simple view Which actor A has more power? (left or right)24 [Hanneman & Riddle]
    25. 25. Power and Centrality: a more complicated view 1 2 2 3 1 3 4 4 5 5 Chris Pat Who has more Power?25
    26. 26. 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 index26
    27. 27. 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 bridge27
    28. 28. 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 innovation28
    29. 29. Clustering29
    30. 30. 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 age30
    31. 31. 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 connected31
    32. 32. 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 Internet32
    33. 33. Network patterns: single hub (a), multiple hub (b), random (c)33
    34. 34. Network patterns: Core / periphery34
    35. 35. Now explore the application of SNA in KM Social Capital Communities of Practice Knowledge Network Analysis KNA analysis techniques Theory on knowledge sharing networks35
    36. 36. Formal organization vs. informal organization [Cross, Parker, Prusak, Borgatti, 2001] [Brown & Duguid, 1991 ]36
    37. 37. Dimensions of Social Capital (Nahapiet & Ghosal, 1998) Structural •Access to resources (network ties) • (impersonal) properties •Network configuration of the network • Trust, Identification Relational •Norms • kind of personal relationships • Obligation (reciprocity) and expectations Cognitive • Common language • resources providing • Shared stories shared context •Narratives37
    38. 38. 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)38
    39. 39. 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 organization39
    40. 40. Bottleneck analysis Lack of learning relations40 Clustering based on Girvan Newman algorithm
    41. 41. 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 Network Performance Structure41
    42. 42. 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. 3.342
    43. 43. Research model Connectedness H1+ Density H1a- Reciprocity H2a- Performance H2b+ Centralization index H3a+ Efficiency H3b- Transitivity43
    44. 44. 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)44
    45. 45. 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?45
    46. 46. 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 18 users (0.002%) 20% 8,000 80% Not uncommon ... Twitter:50%of all the followingattention is directed to 20,000 (0.05%) of users 35,000 users 55,000 posts46
    47. 47. Edge-ratio analysis Most active sub-category (306) 2009_147
    48. 48. Edge-ratio analysis Most active sub-category (306) 2009_248
    49. 49. Edge-ratio analysis Most active sub-category (306) 2009_349
    50. 50. Edge-ratio analysis Most active sub-category (306) 2009_450
    51. 51. Edge-ratio analysis Most active sub-category (306) 2010_151
    52. 52. Edge-ratio analysis Most active sub-category (306) 2010_252
    53. 53. Edge-ratio analysis Most active sub-category (306) 2010_353
    54. 54. Some interesting edge-ratio trends 3.00 120% 2.50 100% 2.00 80% post/user edges between newcomers % 1.50 60% edges between oldies % edges between old and new % newcomer node/edge ratio 1.00 40% 0.50 20% 0.00 0% 1 2 3 4 5 6 7 854
    55. 55. 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. 1877-1889.55