Version history

Version 3.2.0

Improvements

  • A new dialog window is introduced to replace the old one. When multiple screens are used, the new dialog window is always placed on the screen where the DiscretePath window with the canvas is displayed. The old dialog window was usually placed on the main screen (f.ex. the screen of the portable computer).
  • Remark: The ‘Open- and Save-file’ dialog windows may still appear on the main computer screen and not on the monitor with the DiscretePath windows.
  • The calculation of the clustering based on ‘hitting time’, is more responsive to pressing the ‘ESC’ button to stop the clustering.
  • Windows opened from the menu, contextual menus, calculation buttons, etc. remain above the main window.

Bug fixes

  • A rare bug when importing an adjacency matrix has been fixed.
  • When an existing group vertex is extended with new vertices, the name of the group vertex does not need to be changed.
  • Random walk: the fields for the start and end vertices can be filled in manually.

Version 3.1.1

Improvements

  • Version 3.1.1 takes the first step in converting DiscretePath to a multicore application.
  • The computation of clustering by hitting time is now two to five times faster, depending on the number of available cores and the size of the graph.
  • The computation of the centralities is at least twice as fast.

Version 3.0.0

Improvements

  • The matrices constructed in the hitting time and nearest neighbor clustering algorithms can be reused when only the value of the slider that determines stronger or weaker clustering has been changed. This results in significant time savings for re-clustering (especially for hitting time clustering).
  • After clustering, each of the clusters can be saved as a separate graph that can be used for further analysis (further clustering, centrality, …).
  • The ‘Nearest Neighbor’ clustering algorithm has been significantly improved, especially the part that takes into account the weights of the edges.
  • Memory managing has been further optimized.
  • The help function has been further expanded.

Bug fixes

  • A rare bug in the ‘Save’-procedure has been fixed.

Version 2.8.2

Bug fixes

  • In order to cluster based on the ‘hitting time’, and the weight of the edges had to be taken into account, the radio buttons of the palette window
    • Higher weight -> stronger clustering
    • Lower weight -> stronger clustering

were implemented inversely in the calculation of the clustering. This has been corrected.

Version 2.8.1

Improvements

  • The help function (right-click on controls) has been expanded.
  • The menu bar has been modified: a ‘Beyond DP’ menu item (in the ‘Help’ menu) has been added that leads to a DiscretePath web page with downloads of Wolfram Mathematica files with extended capabilities, complementing DiscretePath.

Bug fixes

  • Drawing of a random graph: if the final drawing was canceled, the buttons ‘Draw graph’ of the palette window are now reactivated (in previous versions, these buttons could only be reactivated by opening and then closing the window for drawing random graphs).
  • The menu item: ‘Help’ -> ‘Reset preferences and close DiscretePath’ could produce an error message after closing the application (had no effect on the application). This error has been fixed.

Version 2.8.0

Improvements

  • The application has been made more responsive: during long calculations, the operating system does not perceive the application as unresponsive.
  • Clusters are numbered increasing with decreasing size (largest cluster = cluster 1, …) .
  • The process of contracting clusters into ‘group vertices’ can now be aborted.
  • The clustering result can be exported as a tab-delimited text file (can be imported into a spreadsheet).

Bug fixes

  • Cluster result in the ‘Info’ tab of the Palette window: The results can be sorted correctly by cluster number and clicking on the rows highlights the correct vertices in the canvas.

Version 2.7.2

Improvements

  • The import of x,y-coordinates has been made more flexible: the data can contain vertices that are not present in the graph.
  • Major speed improvement of the clustering method based on hitting time.
  • Graph clustering can be done sequentially by collapsing the vertices of each cluster into a group vertex. In this way, the number of clusters can be reduced and new insights into the deeper network structure can be obtained.

Bug fixes

  • When group vertices were extracted into their components and/or collapsed again, the weight of the edges between the group vertices could be incorrect. This has now been resolved.

Version 2.6.7

Improvements

  • Further improvement of the clustering methods based on ‘nearest neighbor’ and ‘hitting time’ (more accurate assignment of the vertices to a cluster).
  • Various minor code adjustments for speed improvements.

Version 2.6.5

Improvements

  • Both the clustering on ‘Hitting time’ and ‘Nearest neighbor’ are standardized for the order of the vertices from which the clustering is started. This makes the clustering less sensitive to the way the graph was constructed.
  • Clustering via ‘Hitting time’ has been made more efficient, although clustering larger graphs can still take a long time.
  • Memory usage when calculating clusters has been optimized.

Bug fixes

  • The presence of self-loops does not interfere with clustering based on either ‘Hitting time’ and ‘Nearest neighbor’.

Version 2.6.3

Improvements

  • The performance of the ‘nearest neighbor’ clustering method has been improved.

Bug fixes

  • A bug in the ‘nearest neighbor’ and ‘hitting time’ clustering algorithms has been fixed.

Version 2.6.1

Improvements

  • A new effective clustering method based on the ‘hitting time distribution’ of the vertices has been added.
  • The ‘nearest neighbor’ clustering method has been updated.

Bug fixes

  • The help window that appears when connecting two vertices with an edge is correctly positioned at the mouse pointer.
  • The error window that sometimes appears after closing the application no longer appears (MacOS).

Version 2.5.5

Improvements

  • ‘Help info’ updated.
  • Added AI for calculating cliques and centralities.

Bug fixes

  • Corrected clustering with ‘Take weight of edges into account’ when vertices connected by edges with lower weights have stronger clustering.

Version 2.5.4

Improvements

  • For complex graphs, constructing a random walk with a start and end vertex is much faster.
  • Listing the vertices or edges in the ‘Info tab’ of the Palette window can be canceled with the ‘ESC’ key.
  • When the orientation of an edge is changed in the ‘Layout tab’ of the Palette window, the change is visualized in real time in the canvas.

Bug fixes

  • No error message is displayed when the application is closed.
  • When a random walk is not possible, the graph is not frozen.

Version 2.5.3

Improvements

  • The gray color of the edges can be set directly from the Palette window (Layout tab).
  • The edge drawing has been optimized.
  • Opening the application is faster.
  • Refreshing the canvas is faster.
  • The font size of controls and titles throughout the application has been increased.

Version 2.5.0

Improvements

  • Calculation of centrality:
    In ‘vertex betweenness’, ‘edge betweenness’ and ‘closeness centrality’ the edge weights have a negative influence on centrality by default. Now the options have been made available to calculate the centralities in which the edge weights have a neutral, negative or positive influence on the centralities.
    This is useful if, for example, the flow through the edges is an important binding property (number of contacts between people, number of people traveling between cities, …). For more information see the ‘User’s guide’.
  • Importing data:
    The cleaning of data imported from spreadsheets has been further expanded. The user is also informed where corrections have been or need to be made in the imported data.
  • Canvas refresh has been optimized.

Version 2.4.6

Improvements

  • The cluster module has been optimized (e.g. assignment of vertices to another cluster).
  • Decision tree module: More complex decision trees can be built.
  • Decision tree module: Systematic detection of anomalies in the data format of the imported data.

Bug fixes

  • Decision tree module: Missing data in the validation and analysis data is detected and the samples with missing data are removed from the data set.
  • Decision tree module: If more than 100 samples are removed from the training dataset (missing data, wrong format data, …), no error message is displayed and model building can continue.

Version 2.4.5

Improvements for A.I. module: ‘Decision tree’

  • In the ‘Palette window’ the choice is available to build a decision tree with one or more numerical attributes and/or a numerical class, on a simplified or more detailed level.
  • Even if the data does not allow building a good model, a model is always produced. The user can then decide how to proceed.
  • After building the model, a summary of the model’s robustness is displayed in the ‘Info tab’ of the Palette window.

Bug fixes for A.I. module: ‘Decision tree’

  • Missing data in the imported dataset can now be represented by any character in addition to an empty field in the dataset.
  • When data is missing from the imported dataset, the samples can be deleted or kept. In the latter case, the missing data is replaced by imputation. In the previous version, the replacement was not always complete. In this version, the imputation of the missing data is complete.
  • In the previous version, invalid error messages could be displayed. This has been fixed.

Version 2.4.1

This version contains an important update for the 'Decision tree' module.
- The processing of numerical data has been highly optimized.
- The import of inconsistent data is handled efficiently.

Version 2.4.0

Improvements

An 'Artificial Intelligence' module has been added ('A.I.' tab of the Palette window). This module covers A.I. algorithms that use graphs in some form. This release includes the first supervised learning A.I. algorithms: "Decision Trees". The decision tree algorithms can handle both categorical and numerical data and can be used for both classification and regression tasks.

Bug fixes

'Random walks' now works with directed graphs and no errors occur when using edge weight as a walk parameter.

Version 2.3.0

DiscretePathPLUS is discontinued. All the functions specific to DiscretePathPLUS are now available in the free software DiscretePath. In the future, only DiscretePath will be maintained and further developed. In 2023, we will focus on introducing machine learning techniques and algorithms to analyze data using graphs as a tool and to take graph analysis to the next level. We recommend the DiscretePathPLUS user to switch to the free software DiscretePath. 

Improvements for DiscretePath

  • Vertices can be organized in groups (improves graph layout, visualization and analysis).
  • Vertices can be organized in sub and super categories.
  • X,Y-coordinates of the vertices can be imported. For example, this way the vertices that represent locations on a map can be set to their latitude and longitude coordinates.
  • Calculation of the ‘maximum clique’ and ‘all cliques’. The maximum clique is used in many applications to identify the most connected and interactive parts in biological, collaboration, interaction, technological, social, ….. networks. The identification of all cliques allows to identify also the smaller interactive parts of a network. For the calculation of ‘all cliques’ a lower limit can be set (minimum number of vertices that the clique must contain). If a graph contains multiple maximum cliques, ‘all cliques’ allows them to be identified.

The last version can be downloaded at the menu item Software

To explore the features of DiscretePath consult the user guide (User guide)

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