1. Social network analysis
Knowledge management
Social network analysis

2. Linking (knowledge aggregation) mechanism
Collective knowledge can be more, or less than the sum of the individuals’ knowledge, depending on the mechanisms that translate individual into collective knowledge
Formal (e.g. hierarchy; control and information) vs. informal (learning and innovation)

3. Formal vs. informal network
"within an organization or institution, project groups may have similar information needs even though the individuals who belong to the groups may occupy different positions in the organization and span intra-organization boundaries”

4. SNA, definition
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers or other information/knowledge processing entities.
The nodes in the network are the people and groups while the links show relationships or flows between the nodes.
SNA provides both a visual and a mathematical analysis of complex human systems.
NODE AND RELATIONSHIPS
Other than visualization
The other important component of SNA are measure and index
Created to describe the nature or the structure of network
Measures and indexes,
E-I index
Clustering coefficient
Centrality
Density etc.

5. Components of a social network
Actors
Attributes?
E.g. demographic, psychological/personality traits
Relationships
What are the attributes of relationship ?
Nature
admiration, affection, intensity
Direction
mutual, reciprocal
Strength
warm, cold, strong, weak.

6. Unit of analysis Persons Documents (websites, books, journals…)
Organizations/institutions
Terms and concept ….

7. The link structure of the Web
Hubs and authorities
Source: Kleinberg and Lawrence (2001). The Structure of the Web. Science.

8. The link structure of the Web
Within community link density
Source: Kleinberg and Lawrence (2001). The Structure of the Web. Science.

9. Attribute data Conventional data type-rectangular array Attributes
Name
Sex
Age
occupation
Bob
Male 32
Teacher
Carol
Female 27
waiter
Ted 29
Salesman
Alice 28
airhostess
Subjects

10. Social network data Conventional data type-rectangular array
Attributes
Name
Sex
Age
occupation
Bob
Male 32
Teacher
Carol
Female 27
waiter
Ted 29
Salesman
Alice 28
airhostess
2017/4/26

11. Social network data Network Data type-square array
Actors are described by their relation, not by attributes
Who reports linking whom?
Choice
Chooser
Bob
Carol
Ted
Alice ─ 1

12. What is relational data?
Data concerning relations between things (e.g. agents) rather than the individual properties/attributes of those things.
We might be interested in relations between: people, organisations, nations, towns …anything.
We might be interested in relations based upon: knowledge, emotion, exchange, infection/contamination …anything.

13. Evidence of relationship
Role-based
Affective
Cognitive or interactive
Derived
Flows
See “intro to network analysis”

14. From attribute to matrix
These committee, word occurrence
Thesauri

15. Trace the knowledge flow
The analysis of patterns of relationships between actors and examines the availability of resources and the exchange of resources between these actors.

16. Types of question Who provides the knowledge needed to do your work?
Who do you ask when you have a question involving…?

17. Matrix representation
Sociogram
--- 1
Alice
Ted
Carol
Bob

18. Components of a social network
Actors
Relationships
Content (e.g. personal, formal, collaborative…)
Direction
Directed (symmetrical or asymmetrical, e.g. give or receive)
Undirected (co-authorship, joint membership)
Strength (e.g. intimacy, frequency, duration…)
*Haythornthwaite, pp

19. Some relations are ‘undirected’ (e. g
Some relations are ‘undirected’ (e.g. Join the same committee, co-authorship). This is recorded in the matrix.
Tom
Sally
Alice 1

20. Some relations are ‘directed’ (e. g
Some relations are ‘directed’ (e.g. Liking or consult knowledge) and thus not necessarily reciprocated. This is recorded in the matrix. Tom likes Sally but she doesn’t like him. She likes Alice. Or citing and cited
Tom
Sally
Alice 1 ?
In this case Tom’s relation to Sally and hers to him are distinct and should be treated independently

21. Note that in this case there is unnecessary repetition of information: if Tom share info with Sally then Sally share info with Tom (symmetrical, higher threshold)
Tom
Sally 1
We need to be mindful of this in any calculations we may make. We have one relationship here, not two.

22. A simple relational matrix in which presence/absence of a relation is indicated by a 1 or 0 respectively: who share info with whom (non-symmetrical)?
Tom
Dick
Sally
Fred
Alice 1

23. Tie strength: relations may be weighted in ordinal/interval manner: e
Tie strength: relations may be weighted in ordinal/interval manner: e.g. 0 = ‘Don’t like’, 1=‘like’, 2=‘really like’; or telephones n times per week.
Tom
Dick
Sally
Fred
Alice 2 1 5 4 3 8

25. Essentially we are mapping the flow of information/influence on a graph
Influence (direction, distance, kinds)
And use measurements or metrics associated with the graph to represent the configuration or the makeup of a group or a community (the community could be huge)

26. Agent vs. structure Marco vs. Micro view of social capital
Whole networks
The ties that all members of an environment maintain with all others in the environment.
Egocentric networks
How many ties individual actors have to others, what types of ties they maintain.. etc.

27. Network attributes Cohesion Range Prominence Structural equivalence
Grouping actors according to strong relationship with each other
Connection and density
Range
Indicating the extent of an actor’s network
Prominence
Who are the key players
Structural equivalence
Grouping actors according to similarity in relations with others
Brokerage
Indicating bridging connections to other networks

28. Path
A walk with entirely distinct nodes and lines, i.e. no node or line can included more than once
For example:
Is a path?
In geometry, a diameter of a circle is any straight line segment that passes through the center of the circle and whose endpoints are on the circle
Adopted from Borgatti, 2010

29. Geodesic
Several paths may exist between two nodes, but the shortest path between them is the geodesic
The geodesic distance between “10” and “4”?
The minimum distance is very interesting because it allows us to see how many connections and how many nodes are intermediaries in a relationship between two actors of a network
How efficient can information be transferred?
Etc.

30. Average Distance
Average geodesic distance (shortest) between all pairs of nodes
Also known as average shortest path length
Average distance = 2.4
Average distance = 1.9
Adopted from Borgatti, 2010

31. Network density
Percentage of number of ties to the total possible ties
Consider binary or valued; direct and non-directed

32. Density
The actual number of connections in a network expressed as a proportion of the total possible number of connections.
A figure between 0 and 1.
High density should generate greater: trust, cultural homogeneity and diffusion speed.
Not easy to make meaningful comparisons of density across networks of different sizes (or involving different types of relations).

33. Cohesion: sub-structures
Cliques
fully interconnected subgroups of actors
Cluster, faction, K-core
subgroups of highly interconnected actor

34. Clique
The maximal number of actors who have all possible ties present among them themselves
N-cliques, which define an actor as a member of a clique if they are connected to distances greater than one (more inclusive) Haythornthwaite, p

35. K-core
K-cores = a subset of vertices within a component, all of whose members enjoy a specified number of relations (=‘k’) with the others: e.g. a 6-core is a subset, all of whose members enjoy relations with at least 6 of the other members.

36. Raise “k”, raise the threshold
Original graph
Given a graph, the 0-core is exactly this graph itself, and the 1-core is the subgraph excluding all the isolated nodes.
A node of degree larger than or equal to k may not appear in the k-core since some of its neighbors could be previously removed.
Generally speaking [25, 26], a core of higher coreness is considered to be more central. We denote by N(k) the number of nodes in the k-core, the highest coreness, kmax, is defined as the maximal k that keeps N(k) larger than zero. That is to say, kmax is the highest coreness corresponding to a nonempty core.
Zhang et al. (2008)

37. Identifying key players
Based on individual actors’ interaction patterns with others in the network, certain unique structure positions can be identified.

38. Information gatekeeper
“The well-structure network can act as a screening device in the face of information overload, include others who can be bought into an opportunity, and deliver information early, providing the opportunity to act on the information before it is widespread or obsolete.”

39. Information broker
“A player with a network rich in information benefits has contacts: (a) established in places where useful bits of information are likely to air, and (b) providing a reliable flow of information to and from these places.” (Burt, 1992).

40. Centrality Network view of “power”
Manifest itself in social relationship
An ego’s power is alter’s dependence
Centrality analysis identifies individuals having a favored position, having more opportunities and fewer constraints, and presumably possess more power
POWER AND DEPENDECNE (e.g. CORE/PERIPHERY ) structure
Your supervisor might not be able to hurt you directly,
s/he is likely to be able to hurt you by presenting you in a negative light to persons who has the say in your promotion/punishment
The “retired professors“ controversy
To utilized their connection to get more grants money

41. Centrality Degree centrality Closeness centrality
Counting the number of paths of length 1 emanating from a node
Closeness centrality
The total geodesic distance from a given node to all other nodes
Betweenness centrality
The extent that that node falls on the geodesic paths between other pairs of nodes
Eigenvector centrality
assigns relative scores to all nodes in the network based on the principle that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.
Radial measure (degree, closeness) assess walks that emanate from or terminate with a given node
Eigenvector centrality
The measures of centrality discussed thus far are useful but they are based strictly on the number and nature of the ties that each node has with other actors. They treat each tie equally regardless of the identity of the nodes in question.
An alternative approach to the measurement of centrality requires us to weigh ties in terms of the centrality of the nodes involved.
In other words, a tie to a highly central actor should count more than a tie to a less central one.

42. Actor degree centrality
Naïve but not entirely useless.

43. Actor closeness centrality

44. Actor betweenness centrality
The mediation function is hurt by the dense
connection in its surrounding

45. Actor eigenvector centrality
Their neighbors make a huge difference
The node on the right might have high closeness and betweenness measure, and has equal degree centrality with the node on the left.
But it has smaller eigenvector centrality