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Pajek.  Pajek is a program, for Windows, for analysis and visualization of large networks having some thousands or even millions of vertices. In Slovenian.

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Presentation on theme: "Pajek.  Pajek is a program, for Windows, for analysis and visualization of large networks having some thousands or even millions of vertices. In Slovenian."— Presentation transcript:

1 Pajek

2  Pajek is a program, for Windows, for analysis and visualization of large networks having some thousands or even millions of vertices. In Slovenian language the word pajek means spider.

3  Pajek should provide tools for analysis and visualization of such networks:  collaboration networks,  organic molecule in chemistry,  protein-receptor interaction networks,  genealogies,  Internet networks,  citation networks,  diffusion (AIDS, news, innovations) networks,  data-mining (2-mode networks), etc.  See also collection of large networks at: 

4  to support abstraction by (recursive) decomposition of a large network into several smaller networks that can be treated further using more sophisticated methods;  to provide the user with some powerful visualization tools;  to implement a selection of efficient (subquadratic) algorithms for analysis of large networks.

5  network – main object (vertices and lines - arcs, edges):  graph, valued network, 2-mode or temporal network  partition  Nominal property of vertices. Default extension:.clu  vector  numerical property of vertices. Default extension:.vec  permutation  reordering of vertices. Default extension:.per  cluster  subset of vertices (e.g. a class from partition). Default extension:.cls.  hierarchy  hierarchically ordered clusters and vertices. Default extension:.hie

6  Network can be defined in different ways on input file. Look at three of them:  1. List of neighbours (Arcslist / Edgeslist)(see test test *Vertices 5 1 ”a” 2 ”b” 3 ”c” 4 ”d” 5 ”e” *Arcslist *Edgeslist 1 5


8  Data must be prepared in an input (ASCII) file. Program NotePad can be used for editing. Much better is a shareware editor, TextPad.TextPad  Words, starting with *, must always be written in first column of the line. They indicate the start of a definition of vertices or lines.  Using *Vertices 5 we define a network with 5 vertices. This must always be the first statement in definition of a network.  Definition of vertices follows after that – to each vertex we give a label, which is displayed between “ and ”.  Using *Arcslist, a list of directed lines from selected vertices are declared (1 2 4 means, that there exist two lines from vertex 1, one to vertex 2 and another to vertex 4).  Similarly *Edgeslist, declares list of undirected lines from selected vertex.  In the file no empty lines are allowed – empty line means end of network.

9  2. Pairs of lines (Arcs / Edges) (see test test *Vertices 5 1 ”a” 2 ”b” 3 ”c” 4 ”d” 5 ”e” *Arcs *Edges 1 5 1

10  Directed lines are defined using *Arcs, undirected lines are defined using *Edges. The third number in rows defining arcs/edges gives the value/weight of the arc/edge.  In the previous format (Arcslist / Edgeslist) values of lines are not defined  the format is suitable only if all values of lines are 1.  If values of lines are not important the third number can be omitted (all lines get value 1).  In the file no empty lines are allowed – empty line means end of network.

11  3.Matrix (see test test *Vertices 5 1 ”a” 2 ”b” 3 ”c” 4 ”d” 5 ”e” *Matrix

12  In this format directed lines (arcs) are given in the matrix form (*Matrix). If we want to transform bidirected arcs to edges we can use “Network>create new network>Transform>Arcs to Edges>Bidirected only”

13  Additionally, Pajek enables precise definition of elements used for drawing networks (coordinates of vertices, shapes and colors of vertices and lines,...).  Example: (see test test *Vertices 5 1 “a” box 2 “b” ellipse 3 “c” diamond 4 “d” triangle 5 “e” empty...

14  Layout of networks  Energy: The network is presented like a physical system, and we are searching for the state with minimal energy  Kamada-Kawai: using separate components, you can tile connected components in a plane  Fruchterman-Reingold: draw in a plane or space and selecting the repulsion factor  Eigen Values: Selecting 2 or 3 eigenvectors to become the coordinates of vertices. Can obtain nice pictures

15  Partitions are used to describe nominal properties of vertices.  e.g., 1-men, 2-women  Definition in input file (see test.clu)see test.clu *Vertices

16  Vectors are used to describe numerical properties of vertices (e.g., centralities).  Definition in input file (see test.vec) *Vertices

17  It is time consuming to load objects one by one. Therefore it is convenient to store all data in one file, called Pajek project file (.paj). (see test.paj)see test.paj  Project files can be produced manually by using “File>Pajek Project File>Save”  To load objects stored in Pajek project file select “File>Pajek Project File>Read”

18  Commands are put to menu according to the following criterion:  commands that need only a network as input are available in menu Net,  commands that need as input two networks are available in menu Networks,  commands that need as input two objects (e. g., network and partition) are available in menu Operations,  commands that need only a partition as input are available in menu Partition...


20  Local view is obtained by extracting sub-network induced by selected cluster of vertices.  Global view is obtained by shrinking vertices in the same cluster to new (compound) vertex. In this way relations among clusters of vertices are shown.  Combination of local and global view is contextual view: Relations among clusters of vertices and selected vertices are shown.

21  Import and export in 1994 among 80 countries are given. They is given in 1000$. (See  Partition according to continents (see Country_Continent.clu)see Country_Continent.clu  1 – Africa, 2 – Asia, 3 – Europe, 4 – N. America, 5 – Oceania, 6 – S. America.  Operations>Extract from Network>Partition  Operations>Shrink Network>Partition

22  Operations>Extract from Network>Partition

23  Operations>Shrink Network>Partition

24  Network>Info>Line Values

25  Network>Create New Network>Transform>Remove>Lines with value>lower than (340000)

26  Download  The latest version of Pajek is freely available, for non-commercial use, at its home page:  Text file into Pajek   WoS to Pajek   Tutorial  Exploratory Social Network Analysis with Pajek  visit Pajek wiki for more information 


28 S519



31  The download link:   The new tutorial slides:  s:wos2pajek07.pdf s:wos2pajek07.pdf

32  Download from:  Unpack it and copy ‘montylingua-2.1’ to C:\Python26\Lib\site-packages  Set up a new environment variable named ‘MONTYLINGUA’ and set the variable value as c:\Python26\Lib\site-packages\MontyLingua- 2.1\Python

33  Download the latest version of WoS2Pajek.   Unpack it, and double click on to show the main interface of program:


35 You can also put all wos files in a folder

36  The current version of WoS2Pajek requires 7 parameters to be given by the user:  MontyLingua directory: path to the directory in which the MontyLingua package is installed;  project directory: where the output files are saved;  WoS file;  maxnum – estimate of the number of all vertices (number of records+number of cited Works) –30*number of records;  step – prints info about each k*step record as a trace; step= 0– no trace.  use ISI name / short name;  make a clean WoS file without duplicates;  boolean list[DE, ID, TI, AB] specifying which fields are sources of keywords.




40  Network/Info/General  Network/Create New Network/Transform/Remove/Loops  Network/Create New Network/Transform/Remove/Multiple lines/Single line

41  Paper citation network  Questions  What are highly cited articles?  The diameter of the network?  What are the major clusters?  More questions?

42  Network/Create Partition/Components/Strong [2]  Operations/Network+Partition/Extract SubNetwork [1-*]  Operations/Network+Partition/Transform/Remove Lines/Between Cluster  Save citestrong.clu

43  Read  Network/2-mode network/2-mode to 1-mode/Columns  Network/Create Partition/Components/Weak [2]  Operations/Network+Partition/Extract SubNetwork[1-*]  Network/Create New Network/Transform/Remove/Loops  (which is a co-author network)  Questions:  The author with highest co-authors?

44  [Read]  Network/Create New Network/Transform/1-mode to 2- mode  Network/2-mode Network/2-mode to 1-mode/Rows  Network/Create Partition/Components/Weak [2]  Operations/Network + Partition/Extract SubNetwork [1-*]

45  [Read]  Network/Create Partitions/Degree/Output  Operations/Network+Partition/Extract subNetwork [1-*]  Network/Create New Network/Transform/1-mode to 2- mode  Network/2-mode network/2-mode to 1-mode/Columns  Network/Create Partition/Components/Weak [2]  Operations/Network+Partition/Extract SubNetwork [1-*]


47  One-mode network  each vertex can be related to each other vertex.  Two-mode network  vertices are divided into two sets and vertices can only be related to vertices in the other set.

48  Suppose we have data as below:  P1: Au1, Au2, Au5  P2: Au2, Au4, Au5  P3: Au4  P4: Au1, Au5  P5: Au2, Au3  P6: Au3  P7: Au1, Au5  P8: Au1, Au2, Au4  P9: Au1, Au2, Au3, Au4, Au5  P10: Au1, Au2, Au5 *vertices "P1" 2 "P2" 3 "P3" 4 "P4" 5 "P5" 6 "P6" 7 "P7" 8 "P8" 9 "P9" 10 "P10" 11 "Au1" 12 "Au2" 13 "Au3" 14 "Au5" 15 "Au5" *edgeslist See

49  The network is transformed into an ordinary network, where the vertices are elements from the first subset, using  “Network>2 mode network>2-Mode to 1-Mode>Rows”.

50  If we want to get a network with elements from the second subset we use  “Network>2 mode network>2-Mode to 1-Mode>Columns”.

51  Basic information can be obtained by “Network>Info>General” which is available in the main window of the program. We get  number of vertices  number of arcs, number of directed loops  number of edges, number of undirected loops  density of lines  Additionally we must answer the question:  Input 1 or 2 numbers: +/highest, -/lowest where we enter the number of lines with the highest/lowest value or interval of values that we want to output.  If we enter 10, 10 lines with the highest value will be displayed. If we enter -10, 10 lines with the lowest value will be displayed. If we enter 3 10, lines with the highest values from rank 3 to 10 will be displayed.

52  Load metformin network to Pajek

53 Ding, Y., Song, M., Han, J., Yu, Q., Yan, E., Lin, L., & Chambers, T. (2013). Entitymetrics: Measuring the impact of entities. PLoS One, 8(8): Entitymetrics is defined as using entities (i.e., evaluative entities or knowledge entities) in the measurement of impact, knowledge usage, and knowledge transfer, to facilitate knowledge discovery.


55  Network/Create New Network/SubNetwork with Paths/Info on Diameter  Pajek returns only the two vertices that are the furthest away.

56  Strongly connected components  Network>Create Partition>Components>Strong  Weakly connected components  Network>Create Partition>Components>Weak  Result is represented by a partition  vertices that belong to the same component have the same number in the partition.  Example 


58  Go to partition weak component,  Partition>make network>random network>Input  Visualize the new random network




62  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 3 that does not contain a cut-vertex.


64  Network/Create New Network/......with Bi-Connected Components stored as Relation Numbers  Bicommponents are stored in hierarchy  Load  Get bicomponents with (14 of them) with component size >3

65  The largest component is 244 airports

66  Hierarchy>Extract Cluster (13), then result is stored in cluster  Draw the cluster

67  Operations>Network+Cluster>Extract SubNetwork

68  The info about the largest cluster (244)

69  Network>Create Partition>Degree>Input  Busy airports

70  A subset of vertices is called a k-core if every vertex from the subset is connected to at least k vertices from the same subset.  K-Cores can be computed using “Network>Create Partitions>K-Core” and selecting Input, Output or All core.  Result is a partition: for every vertex its core number is given.  In most cases we are interested in the highest core(s) only. The corresponding subnetwork can be extracted using “Operations>Extract from Network>Partition” and typing the lower and upper limit for the core number.  Example  See


72  How three nodes are connected  Calculation of local Clustering Coefficients:  Network>Create Vector>Clustering Coefficients>CC1 

73  Degree centrality  Network>Create Partition>Degree, or  Network/Create Vector/Centrality/Degree ;  Example: Metformin network

74  How nodes are connecting different clusters  Betweenness centrality  Network>Create vector>Centrality>Betweenness

75  The betweenness centrality value for each node

76  Closeness centrality  Network>Create Vector>Centrality>Closeness  Showing how one node is close to all other nodes in the network

77  Network/Create New Network/SubNetwork with Paths/.....One Shortest Path between Two Vertices  Enter two vertices  Forget values on lines  Yes, if searching for the shortest path is based on lengths  No, if searching for the shortest path is based o vlaue of lines  Identify vertices in source network  No  Result will be a new subnetwork containing the two selected vertices  Layout>Energy>Kamada Kawai>Fix first and last

78  Network/Create New Network/SubNetwork with Paths/.....One Shortest Path between Two Vertices ( )

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