THE KNOWLEDGE MANAGEMENT STRATEGY IN ORGANIZATIONS. Summer semester, 2016/2017

Transcription

1 THE KNOWLEDGE MANAGEMENT STRATEGY IN ORGANIZATIONS Summer semester, 2016/2017

2 SOCIAL NETWORK ANALYSIS: THEORY AND APPLICATIONS

3 1. A FEW THINGS ABOUT NETWORKS

4 NETWORKS IN THE REAL WORLD There are four categories of networks: social networks, information networks, technological networks, biological networks.

5 SOCIAL NETWORK A social network is a social structure made up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as: friendship, kindness, common interest, financial exchange, dislike, sexual relationships, relationships of beliefs, knowledge or prestige.

6 STANLEY MILGRAM'S EXPERIMENT Developed out of a desire to learn more about the probability that two randomly selected people would know each other. He asked certain residents of Wichita and Omaha to contact and send a folder to a target person by sending it to an acquaintance, who would then do likewise etc., until the target person in Boston was reached. This experiment allow Milgram to measure how many intermediate nodes would be necessary to link the original sender and the target. 42 of the 160 letters supposedly made it to their target, with a median number of intermediates equal to 5.5. Hence the idea of six degrees of separation was born.

7 SIX DEGREES OF SEPARATION T h e B a c o n n u m b e r The Bacon number of an actor or actress is the number of degrees of separation he or she has from Kevin Bacon, as defined by the game. This is an application of the Erdős Number Project concept to the Hollywood movie industry. The computation of a Bacon number for the actor X is a "shortest path" algorithm: Kevin Bacon has a Bacon number of 0. Those actors who have worked directly with Kevin Bacon have a Bacon number of 1. If the lowest Bacon number of any actor with whom X has appeared in a movie is N, X's Bacon number is N B = 2 B = 1 N = 1 Actor X

8 STRONG AND WEAK TIES According to Mark Granovetter, Weak Ties are the most important source of information that contribute to the promotion of the subject and. In contrast to the weak, the strong and the close interpersonal relationships are the channel of information, the least different from that available to the subject himself. This information begins to be duplicated, which reduces its usefulness.

9 STRENGTH OF WEAK TIES Granovetter interviews 54 people who found their jobs via a social tie.

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11 2. THE NOTIONS OF SOCIAL NETWORK ANALYSIS

12 SOCIAL NETWORK

13 DIRECTED GRAPH Jim 2 Anne 1 Mary 3 John 4 Adjacency matrix Node

14 UNDIRECTED GRAPH Jane 2 1 and 2 like each other 2, 3 and 4 are relatives Tom 1 Mike 3 1 and 3 are neighbors 1 and 4 are colleagues Sam 4 Adjacency matrix Node

15 ADDING WEIGHTS TO EDGES Girls school dormitory dining-table partners (Moreno, The sociometry reader, 1960): first and second choices shown

16 GRAPH CHARACTERISTICS 1 N Actors {n 1, n 2,, n g } n1 nj - there is a tie between the pair <n1,nj > (n1,nj) - nondirectional relation <n1,nj> - directional relation g*(g-1) - number of ordered pairs <n1,nj> in directional network g*(g-1)/2 - number of ordered pairs (n1,nj) in nondirectional network

17 GRAPH CHARACTERISTICS 2 Measures FROM IMMEDIATE CONNECTION NEIGHBORHOOD IN-DEGREE is the set of nodes that the node is connected to how many directed links are incident on a node OUT-DEGREE how many directed links originate at a node DEGREE (in- and out-) CENTRALITY number of links incident/originate node FROM THE ENTIRE GRAPH betweenness, closeness

18 2.1. DEGREE CENTRALITY

19 NODE DEGREE Adjacency matrix (A) Node OUTDEGREE: Example: out-degree for node 3 is 1, which we obtain by summing the number of non-zero entries in the 3 rd row INDEGREE: Example: the indegree for node 3 is 2, which we obtain by summing the number of non-zero entries in the 3 rd column

20 DEGREE CENTRALITY OF A NODE Standardization of d Oi and d Ii allows to compare nodes from networks with different sizes. Directed graph Undirected graph

21 DEGREE CENTRALIZATION OF A NETWORK Value of a node n i

22 DEGREE CENTRALIZATION Example: financial trading networks High centralization: one node trading with many others Low centralization: trades are more distributed

23 2.2. CLOSENESS CENTRALITY

24 CLOSENESS CENTRALITY The closeness centrality of a node is based on the total distance between one node and all other nodes, where larger distances make lower closeness centrality scores. The closer a node is to all other nodes, the easier information may reach it. Closeness Centrality (Index of expected arrival time) Normalized Closeness Centrality Sum of Geodesic Distances of each node

25 CLOSENESS CENTRALITY OF A NODE Example

26 CLOSENESS CENTRALIZATION OF A NETWORK Value of a node n i

27 C L O S E N E S S C E N T R A L I T Y Application High closeness centrality individuals tend to be important influencers within their local network community. They may often not be public figures to the entire network of a corporation or profession, but they are often respected locally and they occupy short paths for information spread within their network community.

28 2.3. BETWEENNESS CENTRALITY

29 BETWEENNESS CENTRALITY Degree and closeness centrality: How easily can information reach a person? Betweenness centrality and centralization rests on the idea that a person is more central if he or she is more important as an intermediary in the communication network: 1.How crucial is a person to the transmission of information through a network? 2.How many flows of information will be broken if a person stops passing on information or disappears from the network? 3.To what extent may a person control the flow of information due to his/her position in the communication network?

30 BETWEENNESS CENTRALITY A person is more central if he or she is more important as an intermediary in the communication network. How many shortest geodesic links between two actors n j and n k the actor n i contains? (n j n i n k ) g ik - the number of geodesics from n j to n k g jk (n i ) - the number of geodesics that contain point n i as an intermediary in the geodesics from n j to n k, then: is the probability that distinct actor n i involved in communication between two actors n j and n k

31 BETWEENNESS CENTRALITY Usually normalized by: number of pairs of vertices excluding the vertex itself Betweennes Centralization: Value of a node n i

32 BETWEENNESS CENTRALITY K=1 K=2 K=3 J=5 1/1 2/2 1/1 J=6 1/1 2/2 1/1 J=7 2/2 4/4 2/2 J=8 2/2 4/4 2/2 J=9 2/2 4/4 2/2

33 BETWEENESS CENTRALITY Application High betweenness individuals are often critical to collaboration across departments and to maintaining the spread of a new product through an entire network. Because of their locations between network communities, they are natural brokers of information and collaboration.

34 2.4. EIGENVECTOR CENTRALITY

35 EIGENVECTOR CENTRALITY A person s importance is defined by his/her friends importance. More precisely, centrality of a node is proportional to the sum of scores of its neighbors.

36 EIGENVECTOR CENTRALITY Google s Pagerank is a variant of the eigenvector centrality. PageRank is an algorithm used by Google Search to rank websites in their search engine results. Page B Page C Page E

37 SUMMARY

38 M E A S U R E S Conclusion Centrality measure DEGREE CLOSENESS BETWEENNESS Interpretation in social networks How many people can this person reach directly? Who has the shortest distance to the other actors? Who controls knowledge flows?

39 NETWORK QUANTITATIVE CHARACTERISTICS NETWORK Directed / undirected Weighted / not weighted One network EACH NODE Eigenvector Centrality Normalized Networks comparison Eigenvector Number of nodes & edges (size) Degree Centralization Degree, In-degree, Out-degree Normalized degrees Closeness Centralization Closeness Centrality Normalized Closeness Betweenness Centralization Betweenness Centrality Normalized Betweenness

40 ASSIGNMENTS

41 DIFFERENT VISUALIZATION METHODS Analyze two projects describing a network

42 ASSIGNMENT 1 Part 1. Building a network 1. Think of any network You would like to describe. 2. Prepare the data about relationships in this network. 3. Draw the Network, using Gephi. 4. Define: Degree and Weighted Degree measures for each node, Closeness Centrality for each node, Betweenness Centrality for each node, Eigenvector Centrality for each node, Density of the network Modularity of the network (communities inside the network). 5. Make conclusions about relationships in the network.

43 ASSIGNMENT 1 Part 2. Centrality Generally different centrality metrics will be positively correlated When they are not, there is, probably, something interesting about the network Suggest possible node positions to fit each square Low Degree High Degree - Low Closeness High Closeness - Low Betweenness High Betweenness -

44 ASSIGNMENT 1 Example: International Week network

45 ASSIGNMENT 2 Facebook Network 1. Choose an open group/page on Facebook. 2. Find the Netvizz application in Facebook search panel. 3. Choose the group data or page data option (in accordance with the choice in 1). 4. Define the page/group ID. 5. In data to get choose full data. 6. In case of page data analysis, choose posts by page and users option. 7. Download the zip archive. In Gephi, You will use only the GDF file.

46 ASSIGNMENT 2 Facebook Network 1. For the obtained graph define all the possible measures. 2. Identify, which type of post causes the highest engagement? If possible, find out what information does this post contain? 3. Evaluate the general effectiveness of the page?

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