Knowledge Management Prof dr Nada Lavra Topics Degree
![Knowledge Management Prof. dr. Nada Lavrač Topics: - Degree centrality/centralization - Closeness centrality/centralization - Knowledge Management Prof. dr. Nada Lavrač Topics: - Degree centrality/centralization - Closeness centrality/centralization -](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-1.jpg)
![Center and Periphery Social networks: - looking for a way of flow of the Center and Periphery Social networks: - looking for a way of flow of the](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-2.jpg)
![Communication ties in sawmill Communication ties in sawmill](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-3.jpg)
![Distance - Information may easily reach vertices (people) who are central in communication network Distance - Information may easily reach vertices (people) who are central in communication network](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-4.jpg)
![Distance a star network b line network Distance a star network b line network](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-5.jpg)
![Distance – degree centrality/centralization reachability of a vertex inside network - In this case Distance – degree centrality/centralization reachability of a vertex inside network - In this case](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-6.jpg)
![Distance – degree centrality/centralization reachability of a vertex inside network a star network (most Distance – degree centrality/centralization reachability of a vertex inside network a star network (most](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-7.jpg)
![Communication ties in sawmill Communication ties in sawmill](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-8.jpg)
![Distance – degree centrality/centralization reachability of a vertex inside network Using “Pajek” on our Distance – degree centrality/centralization reachability of a vertex inside network Using “Pajek” on our](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-9.jpg)
![Distance – degree centrality/centralization Using “Pajek” on our simple network: Net > Partitions > Distance – degree centrality/centralization Using “Pajek” on our simple network: Net > Partitions >](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-10.jpg)
![Distance – degree centrality/centralization on our assignment Ilp. Net 2 all Distance – degree centrality/centralization on our assignment Ilp. Net 2 all](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-11.jpg)
![Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced Who are the Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced Who are the](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-12.jpg)
![Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-13.jpg)
![Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced > centrality centralization Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced > centrality centralization](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-14.jpg)
![Distance – Geodesic Two vertices (people) are connected if path exists form one to Distance – Geodesic Two vertices (people) are connected if path exists form one to](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-15.jpg)
![Distance – closeness centrality/centralization reachability of a vertex inside network The closeness centrality of Distance – closeness centrality/centralization reachability of a vertex inside network The closeness centrality of](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-16.jpg)
![Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” Q: I’ll go and work Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” Q: I’ll go and work](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-17.jpg)
![Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” A: I’ll look into subjects Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” A: I’ll look into subjects](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-18.jpg)
![Distance – betweenness centrality/centralization The betweenness centrality of a vertex is the proportion of Distance – betweenness centrality/centralization The betweenness centrality of a vertex is the proportion of](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-19.jpg)
![Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-20.jpg)
![Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek”, reduced number of vertices and Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek”, reduced number of vertices and](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-21.jpg)
![Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-22.jpg)
![Broker and Bridges The bridges and lines who bridge structural holes between other have Broker and Bridges The bridges and lines who bridge structural holes between other have](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-23.jpg)
![Broker and Bridges Simple example using “Pajek” Broker and Bridges Simple example using “Pajek”](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-24.jpg)
![Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp. Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp.](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-25.jpg)
![Broker and Bridges Ilp. Net 2 using “Pajek” Broker and Bridges Ilp. Net 2 using “Pajek”](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-26.jpg)
![Broker and Bridges Ilp. Net 2 – enlarged part Broker and Bridges Ilp. Net 2 – enlarged part](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-27.jpg)
![Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp. Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp.](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-28.jpg)
![Index of literature and acknowlegement - Exploatory Social Network Analysis with Pajek; W. De Index of literature and acknowlegement - Exploatory Social Network Analysis with Pajek; W. De](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-29.jpg)
- Slides: 29
![Knowledge Management Prof dr Nada Lavrač Topics Degree centralitycentralization Closeness centralitycentralization Knowledge Management Prof. dr. Nada Lavrač Topics: - Degree centrality/centralization - Closeness centrality/centralization -](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-1.jpg)
Knowledge Management Prof. dr. Nada Lavrač Topics: - Degree centrality/centralization - Closeness centrality/centralization - Betweenness centrality/centralization - Brokers and Bridges
![Center and Periphery Social networks looking for a way of flow of the Center and Periphery Social networks: - looking for a way of flow of the](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-2.jpg)
Center and Periphery Social networks: - looking for a way of flow of the information - ways of diffusion and retrival of the information Concepts of social network analisys: - centrality (individual vertices) - centralization (entire network)
![Communication ties in sawmill Communication ties in sawmill](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-3.jpg)
Communication ties in sawmill
![Distance Information may easily reach vertices people who are central in communication network Distance - Information may easily reach vertices (people) who are central in communication network](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-4.jpg)
Distance - Information may easily reach vertices (people) who are central in communication network - Simplest indicator of centrality of vertex is number of its neighbors (connected) - Problem: Given a fix number of lines what is the most efficient structure to exchange the information?
![Distance a star network b line network Distance a star network b line network](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-5.jpg)
Distance a star network b line network
![Distance degree centralitycentralization reachability of a vertex inside network In this case Distance – degree centrality/centralization reachability of a vertex inside network - In this case](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-6.jpg)
Distance – degree centrality/centralization reachability of a vertex inside network - In this case star network the most efficient structure (given the fix number of lines) - Network is more centralized if the vertices vary more with respect to their centrality Defining degree of centralization Who has the more sources of information at its disposal? - The degree centrality of vertex is its degree Degree centralization of a network is the variation in the degrees of vertices divided by the maximum degree which is posible in the network of the same size
![Distance degree centralitycentralization reachability of a vertex inside network a star network most Distance – degree centrality/centralization reachability of a vertex inside network a star network (most](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-7.jpg)
Distance – degree centrality/centralization reachability of a vertex inside network a star network (most efficient) degree centralizaton: v 1 degree = 5 (max degree) v 2 to V 5 degree = 1 (min degree) => v 1 contributes 1 x (5 -5) and v 2 to v 5 contributes 5 x (5 -1) => so 20 is the maximum degree variations => 20/20 = 1 max degree centralization b line network: v 7 and v 12 degree = 1 v 8, v 9, v 10, v 11 degree = 2 max degree in this network => v 7 and v 12 contributes 2 x (2 – 1) and V 8 to v 11 contibutes 4 x (2 – 2) => 2 / 20 (max degree in the network of the same size) = 0, 1
![Communication ties in sawmill Communication ties in sawmill](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-8.jpg)
Communication ties in sawmill
![Distance degree centralitycentralization reachability of a vertex inside network Using Pajek on our Distance – degree centrality/centralization reachability of a vertex inside network Using “Pajek” on our](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-9.jpg)
Distance – degree centrality/centralization reachability of a vertex inside network Using “Pajek” on our simple network: Net > Partitions > Degree => centralization degree of network • • All degree centrality of 1. C: Down. LoadsFirefoxPajek - All dataSawmill. net (36) ---------------------------------------Working. . . ---------------------Network All Degree Centralization = 0. 28908 ---------------------Time spent: 0: 00
![Distance degree centralitycentralization Using Pajek on our simple network Net Partitions Distance – degree centrality/centralization Using “Pajek” on our simple network: Net > Partitions >](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-10.jpg)
Distance – degree centrality/centralization Using “Pajek” on our simple network: Net > Partitions > Degree => degree centrality of vertices • • • • • • • • • 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 0. 0285714 - HP-1 0. 0857143 - HP-2 0. 0285714 - HP-3 0. 1142857 - HP-4 0. 1428571 - HP-5 0. 0857143 - HP-6 0. 1428571 - HP-7 0. 1142857 - HP-8 0. 0857143 - HP-9 0. 0571429 - HP-10 0. 0571429 - HP-11 0. 3714286 - HM-1 (Juan) 0. 1142857 - HM-2 0. 1142857 - HM-3 0. 0571429 - HM-4 0. 1142857 - HM-5 0. 0857143 - HM-6 0. 0857143 - HM-7 0. 0857143 - HM-8 0. 1714286 - HM-9 0. 0285714 - HM-10 0. 0571429 - HM-11 0. 1428571 - EM-1 0. 0857143 - EM-2 0. 0857143 - EM-3 0. 0285714 - EM-4 0. 1142857 - EM-5 0. 0857143 - Y-1 0. 0285714 - Y-2 0. 0571429 - Forester 0. 2000000 - Mill Manager 0. 1142857 - Owner 0. 0857143 - Kiln operator 0. 0571429 - EP-1 0. 0857143 - EP-2 0. 1428571 - EP-3
![Distance degree centralitycentralization on our assignment Ilp Net 2 all Distance – degree centrality/centralization on our assignment Ilp. Net 2 all](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-11.jpg)
Distance – degree centrality/centralization on our assignment Ilp. Net 2 all
![Distance degree centralitycentralization on our assignment Ilp Net 2 reduced Who are the Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced Who are the](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-12.jpg)
Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced Who are the most central persons in network; who has the most collaborations? - First we reduced number of vertices to those connected with min two neighbors Net > Transform > Reduction > Degree > All (min. Degree of vertices < 2) From 589 to 416 vertices We removed people who wrote only one article by themselves or pairs of people that wrote article together
![Distance degree centralitycentralization on our assignment Ilp Net 2 reduced Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-13.jpg)
Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced
![Distance degree centralitycentralization on our assignment Ilp Net 2 reduced centrality centralization Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced > centrality centralization](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-14.jpg)
Distance – degree centrality/centralization on our assignment Ilp. Net 2 reduced > centrality centralization Centralization of the network: Net > Partitions > Centrality • • • All degree centrality of 2. All (recursive) degree reduction of N 1 [2] (416) ---------------------------------------Working. . . ---------------------Network All Degree Centralization = 0. 10282 Top 10 central persons in Ilp. Net (sorted using excel) 1 2 3 4 5 6 7 8 9 10 39. 111. 65. 118. 51. 50. 101. 138. 4. 110. 0. 1132530 0. 1036145 0. 0722892 0. 0650602 0. 0481928 0. 0457831 0. 0433735 - MUGGLETON, DZEROSKI, S. BLOCKEEL, H. RAEDT, L. LAVRAC, N. FLACH, P. LAER, W. SRINIVASAN, WROBEL, S. BRUYNOOGHE, S. A. M.
![Distance Geodesic Two vertices people are connected if path exists form one to Distance – Geodesic Two vertices (people) are connected if path exists form one to](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-15.jpg)
Distance – Geodesic Two vertices (people) are connected if path exists form one to another - In undirected network the distance is the number of lines or steps in the shortest path that connect two vertices together - In directed network distance can be different in reverse way A geodesic is the shortest path between two vertices The distance from vertex u to vertex v is the length of the geodesic u to v.
![Distance closeness centralitycentralization reachability of a vertex inside network The closeness centrality of Distance – closeness centrality/centralization reachability of a vertex inside network The closeness centrality of](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-16.jpg)
Distance – closeness centrality/centralization reachability of a vertex inside network The closeness centrality of a vertex is the number of all other vertices divided by the sum of all distances between the vertex and all others Closeness centralization is the variation in the closeness centrality of vertices divided by the maximum variation in the closeness centrality scores possible in a network of the same size. We see that the problem arises if all vertices are not (strongly) connected!
![Distance closeness centralitycentralization Ilp Net 2 using Pajek Q Ill go and work Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” Q: I’ll go and work](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-17.jpg)
Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” Q: I’ll go and work abroad in the institute; to which persons (vertices) should I turn to if I want to work on a subject that person that I trust (vertex) have at least three articles on? - First we reduced number of vertices with less than three articles Net > Transform > Reduction > Degree > All (min. Degree of vertices < 4) From 589 to 143 vertices Calculate closeness centrality (closeness centralization is not possible in our example sice network is not (strongly connected) Net > Vector > Centrality > Closeness
![Distance closeness centralitycentralization Ilp Net 2 using Pajek A Ill look into subjects Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” A: I’ll look into subjects](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-18.jpg)
Distance – closeness centrality/centralization Ilp. Net 2 using “Pajek” A: I’ll look into subjects of articles that these vertices (people) and if it’ll match I’ll try and work with him / her 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 48. 47. 10. 40. 50. 38. 1. 24. 46. 0. 3996198 0. 3758981 0. 3741894 0. 3415837 0. 3360069 0. 3332861 0. 3279748 0. 3215691 0. 3178443 0. 3142049 - RAEDT, L. DZEROSKI, S. MUGGLETON, LAER, W. PAGE, C. JACOBS, N. LAVRAC, N. WROBEL, S. BLOCKEEL, H. BRUYNOOGHE, S. M.
![Distance betweenness centralitycentralization The betweenness centrality of a vertex is the proportion of Distance – betweenness centrality/centralization The betweenness centrality of a vertex is the proportion of](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-19.jpg)
Distance – betweenness centrality/centralization The betweenness centrality of a vertex is the proportion of all geodesics between pairs of other vertices that include this vertex Betweenness centralization is the variation in the betweenness centrality of vertices divides by the maximum variation in betweenness centrality scores in the network of the same size. In social network : to what extent may a person (vertice) control the flow of informaton due to the his / her position inside the communication network?
![Distance betweenness centralitycentralization Ilp Net 2 using Pajek Q I discovered something new Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-20.jpg)
Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new in the area, to whom to turn to in a social network to disperse the quickest possible way information about my discovery Net > Vector > Betweenness Network Betweenness Centralization = 0. 09198 A: This are ttop ten persons with ability to desperse information quickly 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 46. 140. 130. 5. 147. 58. 434. 76. 59. 108. 0. 0931813 0. 0742590 0. 0546139 0. 0459601 0. 0375969 0. 0343720 0. 0234424 0. 0218422 0. 0192772 0. 0181330 - MUGGLETON, S. RAEDT, L. DZEROSKI, S. WROBEL, S. PAGE, C. FLACH, P. ADE, H. BLOCKEEL, H. LAVRAC, N. STEPANKOVA, O.
![Distance betweenness centralitycentralization Ilp Net 2 using Pajek reduced number of vertices and Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek”, reduced number of vertices and](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-21.jpg)
Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek”, reduced number of vertices and multiplied vector for better viewing
![Distance betweenness centralitycentralization Ilp Net 2 using Pajek Q I discovered something new Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-22.jpg)
Distance – betweenness centrality/centralization Ilp. Net 2 using “Pajek” Q: I discovered something new in the area, to whom to turn to in a social network to disperse the quickest possible way information about my discovery? Net > Vector > Betweenness Network Betweenness Centralization = 0. 09198 A: This are ttop ten persons with ability to desperse information quickly 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 46. 140. 130. 5. 147. 58. 434. 76. 59. 108. 0. 0931813 0. 0742590 0. 0546139 0. 0459601 0. 0375969 0. 0343720 0. 0234424 0. 0218422 0. 0192772 0. 0181330 - MUGGLETON, S. RAEDT, L. DZEROSKI, S. WROBEL, S. PAGE, C. FLACH, P. ADE, H. BLOCKEEL, H. LAVRAC, N. STEPANKOVA, O.
![Broker and Bridges The bridges and lines who bridge structural holes between other have Broker and Bridges The bridges and lines who bridge structural holes between other have](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-23.jpg)
Broker and Bridges The bridges and lines who bridge structural holes between other have more control and perform better A bridge is a line whose removal increases the number of components in the network - Deleting a vertex from a network means that the vertex and all lines incident with this vertex are removed from the network - A cut-vertex is a vertex whose deletion increases the number of components in the network - A bi-component is a component of minimum size of three that does not contain a cut-vertex
![Broker and Bridges Simple example using Pajek Broker and Bridges Simple example using “Pajek”](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-24.jpg)
Broker and Bridges Simple example using “Pajek”
![Broker and Bridges Ilp Net 2 Who are the bridges and lines in Ilp Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp.](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-25.jpg)
Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp. Net 2 who bridge structural holes Net > Components > Bi-Components (with a minimum size of 2 so we can look for lines that represents bridges)
![Broker and Bridges Ilp Net 2 using Pajek Broker and Bridges Ilp. Net 2 using “Pajek”](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-26.jpg)
Broker and Bridges Ilp. Net 2 using “Pajek”
![Broker and Bridges Ilp Net 2 enlarged part Broker and Bridges Ilp. Net 2 – enlarged part](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-27.jpg)
Broker and Bridges Ilp. Net 2 – enlarged part
![Broker and Bridges Ilp Net 2 Who are the bridges and lines in Ilp Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp.](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-28.jpg)
Broker and Bridges Ilp. Net 2 Who are the bridges and lines in Ilp. Net 2 who bridge structural holes, articles that two persons work together on? Net > Components > Bi-Components (with a minimum size of 2 so we can look for lines that represents bridges) Root (449) 1 (3) 2 (3) 3 (11) 4 (4) 5 (3) 6 (4). . . 25(2) 26(2) Bridges are bi components of size two in an undirected network, so we can easily find them
![Index of literature and acknowlegement Exploatory Social Network Analysis with Pajek W De Index of literature and acknowlegement - Exploatory Social Network Analysis with Pajek; W. De](https://slidetodoc.com/presentation_image/01695bc750de20d8bc31010896f74f55/image-29.jpg)
Index of literature and acknowlegement - Exploatory Social Network Analysis with Pajek; W. De Nooy, A. Mrvar, V. Batagelj - Internet Links Thank you to Peter Ljubič
G minor enharmonis
Etymologický epigram
Rozbor tyrolské elegie
Genocida significado
Nada te turbe nada te espante salmo 37
Que nada te
Santa teresa de avila
Nada des enharmonis dengan nada.
Estamos aqui de passagem nada trouxemos e nada levaremos
Quien es nada
Nada te turbe, nada te espante salmo 37
Nada te turbe nada te espante oracion
Shared knowledge vs personal knowledge
Knowledge shared is knowledge squared meaning
Knowledge shared is knowledge multiplied meaning
Knowledge creation and knowledge architecture
Contoh shallow knowledge dan deep knowledge
What is a priori knowledge
Book smarts definition
Knowledge and knower
Gertler econ
Software project management topics
Yg dimaksud pemimpin perawatan adalah
Types of information systems
It management project topics
Management topics for project
Engineering management topics
Bin yao
International management double degree
Scientific management