Graph Data Science 2.1.7

GDS 2.1.7 is compatible with Neo 4.3 versions ≥ 4.3.15 and 4.4 ≥ 4.4.9.

For GDS compatibility with previous releases of 4.3 and 4.4, please use please see GDS 2.1.6. The 2.1 series is also incompatible with Neo4j 3.5.x, 4.0, 4.1, and 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.8

Breaking Changes

• Link prediction pipeline training no longer accepts directed graphs. This is because the algorithm & ML techniques used by link prediction pipelines are only defined for undirected graphs.

Bug Fixes

• Fixed a bug in modularityOptimization could incorrectly update modularity values
• Fixed a bug where gds.restore did not correctly read values wrapped in quotes

2.1.6

GDS 2.1.6 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.8

Bug Fixes

• Fixed a bug where relationship types or node labels were not handled correctly when importing previously exported data via Apache Arrow.
• Fixed a bug where gds.graphSage.[stream|write|mutate] did not use the correct relationship weights when run with concurrency > 1.

Graph Data Science 2.1.5

GDS 2.1.5 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.8

Improvements

• Better error handling for K-means

Graph Data Science 2.1.4

GDS 2.1.4 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.8

Bug fixes

• Fixed a bug where Neo4j users with admin role could not see all graphs in the catalog on GDS enterprise.
• Fixed a bug in random graph generation where the resulting graph can end up with an incorrect relationship schema.
• Fixed a bug where gds.graph.list and gds.graph.drop throw a NPE if the catalog contains graphs that have been created with Cypher Aggregation.

Graph Data Science 1.8.8

GDS 1.8.8 is compatible with Neo4j 4.1, 4.2, 4.3 and 4.4 but not Neo4j 3.5.x. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5.

Breaking Changes

• The procedures gds.features.useKernelTracker and gds.features.useKernelTracker.reset have been removed.

Bug fixes

• Fixed a bug in gds.beta.randomWalk.stream where configuring start nodes could lead to AIOOB exceptions.
• Fix a bug in gds.graph.export where the configured database directory would not be respected.
• Fixed a bug with running Triangle Count on filtered graphs.
• Fixed a bug in gds.beta.graphSage when using activationFunction: 'RELU', where the training did not always compute the correct gradient.
• Fixed a bug in gds.louvain.stream that occurrred when the consecutiveIds parameter was enabled.
• Fixed a bug where Neo4j users with admin role could not see all graphs in the catalog on GDS enterprise.

Other Changes

• Updated version of 'com.google.protobuf' to 3.9.12. This fixes a potential Denial of Service issue (GHSA-wrvw-hg22-4m67).

Graph Data Science 2.1.2

GDS 2.1.2 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.7

Bug fixes

• Fixed a bug where checking for business rules around running on a Neo4j cluster could cause the cluster to fail to start.

Graph Data Science 2.1.1

GDS 2.1.1 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.7

Other Updates

• Fixed issue with publishing compatibility artifacts

Graph Data Science 2.0.5

GDS 2.0.5 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.7

Bug Fixes

Fixed a bug in CollapsePath where a provided nodeFilter would be ignored. GH 194

Fixed a bug in RandomWalk where unconsumed stream results could leave GDS in a state where no further operations were possible

Fixed a bug in gds.louvain.stream which could arise when the consecutiveIds parameter was enabled.

Improvements

Improved error message for gds.beta.graph.project.subgraph when comparing expressions with incompatible types and one of them is a literal expression.

Handled exception where centrality scores could not be used to compute the centralityDistribution. Now we only skip the computation of the distribution but the centrality result is accessible.

Graph Data Science 2.1.0

GDS 2.1.0 is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.7

Breaking Changes

• Removed the redundant information of parameter space and split config from the info of the models trained by gds.beta.pipeline.[nodeClassification|linkPrediction].train. The information is now accessible only via the Pipeline Catalog.
• Removed the label parameter from gds.graph.removeNodeProperties.
• Supported config parameters are timeoutInSeconds and concurrency

New Features

• (Enterprise Only) Apache Arrow and Flight RPC can now be used to improve certain import and export tasks:
  • Project a new in-memory graph or Neo4j database via Arrow Flight RPC, for example by using gds.alpha.graph.construct from the GDS Python client
  • Export node, relationship, and graph properties directly via Arrow Flight RPC, for example by using the existing stream*Properties functionality from the GDS Python client
  • Flight RPC is secured with the same authorization and encryption that the Neo4j database is using
• New Algorithm: K-Means Clustering. Added the following procedures:
  • gds.alpha.kmeans.mutate
  • gds.alpha.kmeans.stats
  • gds.alpha.kmeans.stream
  • gds.alpha.kmeans.write
• New Algorithm: Leiden. Added the following procedures:
  • gds.alpha.leiden.mutate
  • gds.alpha.leiden.stats
  • gds.alpha.leiden.stream
  • gds.alpha.leiden.stream
• Added new similarity variant, Filtered Node Similarity, to alpha tier, accepting source and target node filters
  • gds.alpha.nodeSimilarity.filtered.mutate
  • gds.alpha.nodeSimilarity.filtered.stream
  • gds.alpha.nodeSimilarity.filtered.write
  • gds.alpha.nodeSimilarity.filtered.stats
• Added new similarity variant Filtered KNN to alpha tier, accepting source and target node filters
  • gds.alpha.knn.filtered.mutate
  • gds.alpha.knn.filtered.stream
  • gds.alpha.knn.filtered.write
  • gds.alpha.knn.filtered.stats
• Added new procedures for delta stepping:
  • gds.allShortestPaths.delta.stats
  • gds.allShortestPaths.delta.stats.estimate
• Added new procedures for BFS:
  • gds.bfs.stats
  • gds.bfs.stats.estimate
• Added Node Regression Pipelines with the following procedures
  • gds.alpha.pipeline.nodeRegression.create
  • gds.alpha.pipeline.nodeRegression.configureAutoTuning
  • gds.alpha.pipeline.nodeRegression.configureSplit
  • gds.alpha.pipeline.nodeRegression.addLinearRegression
  • gds.alpha.pipeline.nodeRegression.addRandomForest
  • gds.alpha.pipeline.nodeRegression.addNodeProperty
  • gds.alpha.pipeline.nodeRegression.selectFeatures
  • gds.alpha.pipeline.nodeRegression.train
  • gds.alpha.pipeline.nodeRegression.predict.stream
  • gds.alpha.pipeline.nodeRegression.predict.mutate
• Autotuning Support for Machine Learning Pipelines:
  • Added new procedures gds.alpha.pipeline.[nodeClassification|nodeRegression|linkPrediction].configureAutoTuning.
  • Added syntax to specify ranges for parameters in gds.alpha.pipeline.[linkPrediction|nodeClassification|nodeRegression].addRandomForest, gds.beta.pipeline.[linkPrediction|nodeClassification].addLogisticRegression, and gds.alpha.nodeRegression.addLinearRegression
• Additional Machine Learning Pipeline Functionality:
  • Exposed learningRate for the LogisticRegression models, which can be added using gds.beta.pipeline.[nodeClassification|linkPrediction].addLogisticRegression
  • Exposed minLeafSize for RandomForest models, which can be added using gds.alpha.pipeline.[nodeClassification|linkPrediction].addRandomForest
  • Exposed criterion for RandomForestClassification models, which can be added using gds.alpha.pipeline.[nodeClassification|linkPrediction].addRandomForest. Also added support for the ENTROPY impurity criterion.
  • Updated structure of modelSelectionStats yield in gds.beta.pipeline.[linkPrediction, nodeClassification].train.
  • Support OUT_OF_BAG_ERROR metric in gds.beta.pipeline.[linkPrediction, nodeClassification].train which applies only to RandomForest models.
  • Expose batchesPerIteration in gds.beta.graphSage.train to configure the number of batches considered per iteration.
• Cypher Aggregation now accepts any INTEGER value for source and target nodes
• Added ShardedIdMap which adds support for external node ids ranging from 0 to Long.MAX_VALUE.
  • The id map is disabled by default and can be enabled via feature toggle USE_SHARDED_ID_MAP.
• Added procedures for exporting graph properties to the alpha tier
  • gds.alpha.graph.streamGraphProperty
  • gds.alpha.graph.removeGraphProperty
• Exposed a new string config parameter jobId for graph projection and algorithm procedures, which allows for easier tracking of a job via e.g. gds.beta.listProgress.

Bug fixes

• Fixed a bug in gds.beta.pipeline.[nodeClassification|linkPrediction].addNodeProperty where gds.beta.graphSage.mutate could not be added.
• Fixed a bug where the procedures gds.beta.pipeline.linkPrediction.predict.[mutate|stream] threw an error when given the argument initialSampler.
• Fixed a bug with running Triangle Count on filtered graphs that could cause an ArrayIndexOutOfBounds Error.
• Fixed a bug where graphSage.train incorrectly reported didConverge as false.
• Fixed a bug in CollapsePath where a provided nodeFilter would be ignored (GH 194)
• Fixed a bug in gds.louvain.stream when the consecutiveIds parameter was enabled.
• Fixed a bug in RandomWalk where not consuming all stream results could lead to a state where GDS would become unable to run further procedures

Improvements

• When a query is failed by the memory guard, information is logged as well as sent to the user in the raised exception.
• Added new methods to Pregel contexts which allow translating between internal and original node id space.
• Machine learning pipelines
  • gds.beta.pipeline.[nodeClassification|linkPrediction].train.estimate now incorporates memory usage of random forest training into account when applicable.
  • gds.beta.pipeline.[nodeClassification|linkPrediction].predict.[mutate,stream,write].estimate now take random forest prediction memory overhead
  • Improve early validation of graph and prediction pipeline in gds.beta.pipeline.[nodeClassification|linkPrediction].predict.
  • Improve memory estimation for gds.beta.pipeline.[nodeClassification|linkPrediction].train.estimate.
  • Improve memory estimation in gds.beta.pipeline.linkPrediction.train.estimate.
  • Add training method specific debug le...

2.1.0-Preview

GDS 2.1.0-preview is compatible with Neo4j 4.3 and 4.4 but not Neo4j 3.5.x, 4.0, 4.1, or 4.2. For a 3.5 compatible release, please see GDS 1.1.7. For a 4.0 compatible release, please see GDS 1.6.5. For a 4.1 or 4.2 compatible release, please see GDS 1.8.7

Breaking Changes

• Removed the redundant information of parameter space and split config from the info of the models trained by gds.beta.pipeline.[nodeClassification|linkPrediction].train. The information is now accessible only via the Pipeline Catalog.
• Removed the label parameter from gds.graph.removeNodeProperties.
• Supported config parameters are timeoutInSeconds and concurrency

New Features

• (Enterprise Only) Apache Arrow and Flight RPC can now be used to improve certain import and export tasks:
  • Project a new in-memory graph or Neo4j database via Arrow Flight RPC, for example by using gds.alpha.graph.construct from the GDS Python client
  • Export node, relationship, and graph properties directly via Arrow Flight RPC, for example by using the existing stream*Properties functionality from the GDS Python client
  • Flight RPC is secured with the same authorization and encryption that the Neo4j database is using
• New Algorithm: K-Means Clustering. Added the following procedures:
  • gds.alpha.kmeans.mutate
  • gds.alpha.kmeans.stats
  • gds.alpha.kmeans.stream
• New Algorithm: Leiden. Added the following procedures:
  • gds.alpha.leiden.mutate
  • gds.alpha.leiden.stats
  • Gds.alpha.leiden.stream
• Added new similarity variant Filtered Node Similarity to alpha tier, accepting source and target node filters
  • gds.alpha.nodeSimilarity.filtered.mutate
  • gds.alpha.nodeSimilarity.filtered.stream
  • gds.alpha.nodeSimilarity.filtered.write
• Added new similarity variant Filtered KNN to alpha tier, accepting source and target node filters
  • gds.alpha.knn.filtered.mutate
  • gds.alpha.knn.filtered.stream
• Added new procedures for delta stepping:
  • gds.allShortestPaths.delta.stats
  • gds.allShortestPaths.delta.stats.estimate
• Added new procedures for BFS:
  • Gds.bfs.stats
  • gds.bfs.stats.estimate
• Added Node Regression Pipelines with the following procedures
  • gds.alpha.pipeline.nodeRegression.create
  • gds.alpha.pipeline.nodeRegression.configureAutoTuning
  • gds.alpha.pipeline.nodeRegression.configureSplit
  • gds.alpha.pipeline.nodeRegression.addLinearRegression
  • gds.alpha.pipeline.nodeRegression.addRandomForest
  • gds.alpha.pipeline.nodeRegression.addNodeProperty
  • gds.alpha.pipeline.nodeRegression.selectFeatures
  • gds.alpha.pipeline.nodeRegression.train
  • gds.alpha.pipeline.nodeRegression.predict.stream
  • gds.alpha.pipeline.nodeRegression.predict.mutate
• Autotuning Support for Machine Learning Pipelines:
  • Added new procedures gds.alpha.pipeline.[nodeClassification|nodeRegression|linkPrediction].configureAutoTuning.
  • Added syntax to specify ranges for parameters in gds.alpha.pipeline.[linkPrediction|nodeClassification|nodeRegression].addRandomForest, gds.beta.pipeline.[linkPrediction|nodeClassification].addLogisticRegression, and gds.alpha.nodeRegression.addLinearRegression
• Additional Machine Learning Pipeline Functionality:
  • Exposed learningRate for the LogisticRegression models, which can be added using gds.beta.pipeline.[nodeClassification|linkPrediction].addLogisticRegression
  • Exposed minLeafSize for RandomForest models, which can be added using gds.alpha.pipeline.[nodeClassification|linkPrediction].addRandomForest
  • Exposed criterion for RandomForestClassification models, which can be added using gds.alpha.pipeline.[nodeClassification|linkPrediction].addRandomForest. Also added support for the ENTROPY impurity criterion.
  • Updated structure of modelSelectionStats yield in gds.beta.pipeline.[linkPrediction, nodeClassification].train.
  • Support OUT_OF_BAG_ERROR metric in gds.beta.pipeline.[linkPrediction, nodeClassification].train which applies only to RandomForest models.
  • Expose batchesPerIteration in gds.beta.graphSage.train to configure the number of batches considered per iteration.
• Cypher Aggregation now accepts any INTEGER value for source and target nodes
• Added ShardedIdMap which adds support for external node ids ranging from 0 to Long.MAX_VALUE.
  • The id map is disabled by default and can be enabled via feature toggle USE_SHARDED_ID_MAP.
• Added procedures for exporting graph properties to the alpha tier
  • gds.alpha.graph.streamGraphProperty
  • gds.alpha.graph.removeGraphProperty
• Exposed a new string config parameter jobId for graph projection and algorithm procedures, which allows for easier tracking of a job via e.g. gds.beta.listProgress.

Bug fixes

• Fixed a bug in gds.beta.pipeline.[nodeClassification|linkPrediction].addNodeProperty where gds.beta.graphSage.mutate could not be added.
• Fixed a bug where the procedures gds.beta.pipeline.linkPrediction.predict.[mutate|stream] threw an error when given the argument initialSampler.
• Fixed a bug with running Triangle Count on filtered graphs that could cause an ArrayIndexOutOfBounds Error.
• Fixed a bug where graphSage.train incorrectly reported didConverge as false.
• Fixed a bug in CollapsePath where a provided nodeFilter would be ignored.
• Fixed a bug in gds.louvain.stream when the consecutiveIds parameter was enabled.
• Fixed a bug in RandomWalk where not consuming all stream results could lead to a state where GDS would become unable to run further procedures

Improvements

• When a query is failed by the memory guard, information is logged as well as sent to the user in the raised exception.
• Machine learning pipelines
  • gds.beta.pipeline.[nodeClassification|linkPrediction].train.estimate now incorporates memory usage of random forest training into account when applicable.
  • gds.beta.pipeline.[nodeClassification|linkPrediction].predict.[mutate,stream,write].estimate now take random forest prediction memory overhead
  • Improve early validation of graph and prediction pipeline in gds.beta.pipeline.[nodeClassification|linkPrediction].predict.
  • Improve memory estimation for gds.beta.pipeline.[nodeClassification|linkPrediction].train.estimate.
  • Improve memory estimation in gds.beta.pipeline.linkPrediction.train.estimate.
  • Add training method specific debug level logging during the model selection phase of gds.beta.pipeline.linkPrediction.train, gds.beta.pipeline.nodeClassification.train and gds.alpha.pipeline.nodeRegression.train.
  • Improved logging in Link Prediction and Node Classification training.
  • Reduced computational complexity and constant overhead of random forest training, added via gds.alpha.pipeline[linkPrediction|nodeClassification].addRandomFor...