Data input (tutorial)

Normally, visone reads network data from GraphML files, which should never cause any problems. However, in some cases it is necessary to import data stemming from other sources that can, for instance, export adjacency matrices to comma-separated-value (CSV) tables. This tutorial guides you through the various possibilities to input data into visone.

The usual way: read GraphML

GraphML is the usual file format for visone; it encodes the three types of information that are contained in visone networks: network structure, attributes, and graphical information and it is the only format that does so. To read network data from GraphML files use the file menu, click on open..., select files of type .graphml, and click on the ok button.

The other file types are only needed when you want to import data from other sources that cannot output GraphML.

An overview about the other possibilities

Apart from GraphML, visone can read network data from files that are exported by other network analysis software, including UCINET, Pajek, Siena, and some more. Opening these files is also done via the file menu by selecting the appropriate file type. More information about reading these file types is provided in the section about other supported formats.

A more basic option that should be feasible in most situations is to read network data from comma-separated-value (CSV) files. CSV files are plain-text files, for example looking like this

  ;A;B;C;D
 A;0;1;1;1
 B;1;0;1;0
 C;1;1;0;0
 D;1;0;0;0

that can be created by spread sheet editors (such as MS Excel), statistical software, most network analysis software, and many more. However, reading data from CSV files is more error-prone since these files do not come with an unequivocal definition about how to interpret them (rather you have to tell the program how these files should be interpreted). Most of this tutorial is dedicated to the import of CSV files.

If nothing else works, you can make use of the visone R console that allows access to the R environment for statistical computing or you can read data via the KNIME connection that connects visone to a comprehensive data mining workflow tool. Both environments, R and KNIME, provide very general and configurable methods for data input and, in addition, enable you to preprocess and/or filter the data with relatively low effort. We provide more information about these possibilities in the section "if nothing else works".

Yet another possibility to create networks directly in visone is to enter them manually (which is only appropriate if the neworks are small and the data is not yet available in electronic format). This option is illustrated in the tutorial introducing the visual network editor.

The variants of comma-separated-value (CSV) files

A comma-separated-value file can be thought of as a plain-text file that encodes a table, i.e., a data array that has rows and columns - sometimes also referred to as a matrix. In a network context, there are different possibilities to encode information about nodes, links, or attribute information in such tables. The first three (adjacency matrices, link lists, and adjacency lists) provide information about nodes and links and can be opened via the file menu by selecting the appropriate file type, attribute tables provide information about node or link attributes and can be opened via the attribute manager. What follows is a short characterization of these file types; more exhaustive treatments are in the following sections.

• Adjacency matrix files. An adjacency matrix encodes for all pairs of nodes (indexing the rows and columns of the table) whether or not there is a link connecting these nodes. An example is shown in the following.

  ;A;B;C;D
 A;0;1;1;1
 B;0;0;1;0
 C;1;1;0;0
 D;1;0;0;0

The first row and the first column are the labels of the nodes; the remaining part encodes whether there is a link from the node indexing the row to the node indexing the column. For instance, the character 1 in the row indexed by A and the column indexed by B indicates that there is a link going from A to B; the 0 in the row B and column A indicates that there is no link in the reverse direction.

• Link list files. A link list contains as many rows as there are links in the network and (in its most basic form) a link list contains two columns where the entry in the first column is the identifier for the source node and the entry in the second column denotes the target node of the link. The following example

 A;C
 C;B
 B;A
 A;D

defines four links: from node A to node C, from C to B, etc.

• Adjacency list files. An adjacency list has as many rows as there are nodes in the network. Each row may have a different length and the row associated with a node lists all neighbors of this node. For instance, in the following example

 A;2;3;1
 B;0;2
 C;1;0
 D;0

the labels at the beginning of each row are the node identifiers (these ids are optional); A is the label of the node with index 0, B is the label of the node with index 1, etc; the list 2;3;1 following the label A defines that there are links from A to the node with index 2(labeled C), to the node with index 3 (D), and to the node with index 1 (B).

• Attribute tables