- ⬆️ Upgraded to PyTorch Geometric (PyG) 2.0.1. #206
- ⬆️ Upgraded to NetworKit 10.0. #234
- The workspace interface is much faster now. #220
- Now using Conda for managing all dependencies. #209
- 🛠 Fixed an issue with Python boxes returning errors unnecessarily. #225
- 🛠 Fixed an issue with GCS. #224
- 🛠 Fixed CUDA issues with GCN and Node2vec boxes. #234
- ⬆️ Upgraded to Apache Spark 3.1.2. This also brought us up to Scala 2.12, Java 11, Play Framework 2.8.7, and new versions of some other dependencies. #178 #184
- ✅ The "Custom plot" box now lets you use the latest version of Vega-Lite by directly writing JSON instead of going through the Vegas Scala DSL.
- 🔧 Logistic regression models can now be configured to use elastic net regularization.
- Boxes used as steps in a wizard are highlighted in the workspace view by a faint glow. #183
- "Compute in Python" boxes can be used on tables. #160
- Added a "Draw ROC curve" built-in custom box. #197
- 🐎 Performance and compatibility improvements. #188 #194
- 42 algorithms from NetworKit have been integrated into LynxKite. They include new centrality measures, random graph generators, community detection methods, graph metrics (diameter, effective diameter, assortativity), optimal spanning trees and more. (#102, #106, #111, #123)
- 👉 Users can now opt in to sharing anonymous usage statistics with the LynxKite team. (#128)
- Environment variables can be used to override
- ➕ Added a built-in for parametric parameters (
workspaceName) that can be used to force recomputation in wizards. (#131)
⚡️ LynxKite 4.1.0 comes with a big update for our Neo4j support. This has been the most frequently raised point by our new users. Thanks for all the feedback!
- 👍 Neo4j 4.x support.
- Revamped Neo4j import. Instead of importing tables, you can now import a whole graph. (#90)
- ➕ Added Neo4j export. You can export vertex or edge attribute or the whole graph. (#91)
- AVRO and Delta Lake import and export. (#63, #86)
- Added the "Filter with SQL" box as a more flexible alternative to "Filter by attributes".
- Visualization option to not display edges. Great in large geographic datasets.
- "Use table as vertex/edge attributes" boxes are more friendly and handle name conflicts better now.
- ➕ Added aggregation support for Vector attributes. (Elementwise average, sum, etc.)
- ➕ Added an option to disable generated suffixes for aggregated variables.
- 🛠 Fix for edge coloring. (#84)
- 🛠 Fixed issue with interactive tutorials. (#30)
- 🛠 Fixed issue with graph attributes in “Create graph in Python”. (#25)
- 🛠 Fixed issue with non-String attributes in “Use table as graph”. (#26)
- 👍 Replaced trademarked box icons (it was an accident!) with free ones. Also switched to FontAwesome 5 everywhere to get a better selection of icons. (#37)
- 👌 Improved the User Guide. (#38, #39)
We've open-sourced LynxKite!
We took this opportunity to make many changes that break compatibility with the LynxKite 3.x series.
We can help migrate existing workspaces to LynxKite 4.0 if necessary.
- Replaced the separate
Doubleattribute types with
- Instead of the
(Double, Double)attribute type, 2D positions are now represented as
Vector[number]. This type is widely supported and more flexible.
Use "Bundle vertex attributes into a Vector" instead of "Convert vertex attributes to
position" , which is now gone.
- 📇 Renamed "scalars" to "graph attributes". Renamed "projects" to "graphs". These mysterious names
were largely used for historical reasons.
- Removed "Predict with a graph neural network" operation.
(It was an early prototype, long since succeeded by the "Predict with GCN" box.)
- Removed "Predict attribute by viral modeling" box. It is more flexible to do the same
thing through a series of more elemental boxes.
A built-in box ("Predict from communities") has been added to serve as a starting point.
- Made it easier to use graph convolutional boxes: added "Bundle vertex attributes into a Vector"
and "One-hot encode attribute" boxes.
- Replaced the "Reduce vertex attributes to two dimensions" and "Embed with t-SNE" boxes with
the new "Reduce attribute dimensions" box which offers both PCA and t-SNE.
- "Compute in Python" boxes now support
- "Create Graph in Python" box added.
- Inputs and outputs for "Compute in Python" can now be inferred from the code.
🚀 See our changelog for release notes for older releases.
- Replaced the separate
- More accurate progress indicators for box outputs.
- Visualizations can now render edges as undirected straight lines.
- Hover and progress animations for boxes.
- Implemented a lot of common operations on Sphynx speeding up many workspaces significantly.
- Added graph convolutional network operations: "Train a GCN regressor", "Train a GCN classifier", and "Predict with GCN".
- Added "Compute in Python" box.
- Advanced settings in some boxes are hidden behind a click.
- Long legends on visualizations can be scrolled.
- Wizards can be maximized.
- Revamped the "Visualize as slider" feature. The slider now appears on the visualization instead of appearing in the configuration. The slider can affect either the color or the visibility of vertices.
- The currently viewed folder is now stored in the URL, so you can send a link to a specific folder. The default folder after logging in is your user folder.
- Small improvements, like better defaults for graph visualizations and nicer trigger button on import boxes.