Graph Data Science 1.8.5

GDS 1.8.5 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.

Bug fixes

• Fixed a bug where the optimized compressed memory format for GDS graph projections was unavailable

Graph Data Science 1.8.4

GDS 1.8.4 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.

Bug fixes

• Fixed a bug where cypher on gds would try to access node properties as relationship properties and vice versa.
• Fixed a bug where gds.beta.graphSage would produce incorrect results when specifying the nodeLabels filter.
• Fixed a bug where mutate used in conjunction with nodeLabels filter on graphs with multiple node labels and relationship types would sometimes not work correctly.
• Fixed a bug where function gds.alpha.similarity.cosine and procedures gds.alpha.similarity.cosine.[stats,stream,write] returned the absolute value of the cosine computation, instead of the cosine value itself.
• Fixed a bug where an invalid license would prevent the Neo4j database from starting
• Fixed a bug where gds.alpha.closeness might produce wrong results for directed graphs.

Other changes

• Corrected the definition of trainFraction in documentation and removed overly strict validation that required trainFraction + testFraction < 1.

Graph Data Science 1.8.3

GDS 1.8.3 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.

Bug fixes

• Fixed a bug where gds.beta.model.drop could not drop a previously stored model.
• Fixed a bug where gds.beta.model.publish resulted in unusable models if the model was also stored before.
• Fixed a bug where gds.alpha.ml.splitRelationships created relationships with incorrect ids when NodeLabel filtering is used.
• Fixed a bug where gds.alpha.ml.linkPrediction.predict produced relationships with incorrect nodeIds if node labels are filtered.
• Fixed a bug where gds.alpha.ml.pipeline.linkPrediction.predict.mutate produced relationships with incorrect nodeIds if node labels are filtered.
• Fixed a bug where gds.beta.graph.create.subgraph could associate nodes with the wrong properties.

Graph Data Science 1.8.2

Release Date: 13 January, 2022

GDS 1.8.2 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.

Bug fixes

• Fixed a bug where gds.alpha.ml.pipeline.nodeClassification.train would train a model under the wrong username and not be accessible for the actual user.
• Fixed a bug where gds.triangleCount and gds.localClusteringCoefficient might produce wrong results when using a nodeLabels filter.
• Fixed a bug where gds.graph.create or gds.graph.create.subgraph did not release allocated memory which can lead to an OutOfMemoryException, especially when applied in a loop.

Graph Data Science 1.8.1

GDS 1.8.1 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.

Bug fixes

• Fixed a bug where ForkJoin pools were not properly closed which could lead to OOMs using Pregel-based algorithms, e.g. Page Rank.
• Fixed a bug where gds.beta.graphSage could produce incorrect results for small graphs
• Fixed a bug where gds.beta.graphSage could product incorrect results for the pool aggregator
• Fixed a bug where gds.graph.create.cypher would not accept list properties for nodes
• Fixed a bug in gds.beta.graph.create.subgraph where long values greater than 2⁵³ were not properly handled during expression evaluation

Graph Data Science 1.7.3

GDS 1.7.3 is compatible with Neo4j 4.1, 4.2, and 4.3 but not Neo4j 3.5.x, 4.0, or 4.4. 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 4.4 compatible release, please see GDS 1.8.0.

Bug fixes

• Fixed a bug where Node2Vec would produce an AIOOBE on sufficiently large graphs.
• Fixed a bug where ForkJoin pools were not properly closed which could lead to OOMs using Pregel-based algorithms,e.g. Page Rank.

GDS 1.8.0

GDS 1.8 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

• GDS now throws error messages on identifiers with trailing whitespaces to avoid input errors. This affects graphName, modelName, and several property parameters such as nodeWeightProperty or seedProperty.
• We have removed the separate concurrency parameter from the model parameter space in gds.alpha.ml.nodeClassification.train, gds.alpha.ml.linkPrediction.train and gds.alpha.ml.pipeline.linkPrediction.configureParams. The concurrency value in the configuration of the train procedure will be used.
• The procedure gds.alpha.randomWalk.stream has graduated to the beta tier, as gds.beta.randomWalk.stream.
  • Random Walk has been improved and aligned with the Node2Vec implementation. Please consult the documentation to find out about the new configuration options.
  • gds.alpha.randomWalk.stream has been removed.
  • A memory estimation procedure, gds.beta.randomWalk.estimate has been added
• The procedure gds.beta.fastRPExtended has been merged with gds.fastRP.

New features

• Link Prediction
  • Add new link prediction stream procedure gds.alpha.ml.pipeline.linkPrediction.predict.stream.
  • Added probabilityDistribution and samplingStats to the result of gds.alpha.ml.pipeline.linkPrediction.predict.mutate.
  • To improve prediction performance, we’ve added kNN-based approximate search strategy option to link prediction procedures gds.alpha.ml.pipeline.linkPrediction.predict.stream|mutate.
  • Node property steps in Link Prediction pipelines can use a relationship property.
• Node Classification pipelines: similar to link prediction pipelines, we’ve added a pipeline procedure for node classification, where users can define the features, splitting strategy, and model training options. We’ve added:
  • gds.alpha.ml.pipeline.nodeClassification.create
  • gds.alpha.ml.pipeline.nodeClassification.addNodeProperty
  • gds.alpha.ml.pipeline.nodeClassification.selectFeatures
  • gds.alpha.ml.pipeline.nodeClassification.configureParams
  • gds.alpha.ml.pipeline.nodeClassification.configureSplit
  • gds.alpha.ml.pipeline.nodeClassification.train
  • gds.alpha.ml.pipeline.nodeClassification.predict.mutate|stream|write
• New algorithm: Conductance, gds.alpha.conductance.stream, can be used to compute a metric to evaluate the quality of communities identified by community detection algorithms.
• Added support for preserving a relationship property in gds.alpha.ml.splitRelationships.mutate.
• The procedure gds.fastRP has received additional configuration parameters:
  • featureProperties: to configure using node properties as part of the embedding.
  • propertyRatio: to control how much of the embedding is computed from properties.
  • nodeSelfInfluence: allows using each node's initial random vector as a contribution to the node's embedding. Especially useful for graphs with disconnected nodes.

Bug fixes

• Added check that concurrency is meeting determinism constraints for K-Nearest Neighbors whenever randomSeed is overridden.
• Fixed an ArrayIndexOutOfBounds error that could happen in triangle count on some graphs with multiple relationship types.
• Fixed an issue where seeded algorithms (such as WCC) on graphs with multiple node labels could assign seeded communities to new nodes.
• Fixed an issue where KNN did not add candidates to the topK result.
• Fixed an issue where running an algorithm could return incorrect results on graphs filtered with the configuration parameter nodeLabels.
• Fixed an issue where running gds.alpha.ml.pipeline.linkPrediction.train could result in an error on graphs filtered with the configuration parameter nodeLabels.
• Fixed an ArrayIndexOutOfBounds error that could happen in triangle count on some graphs with multiple relationship types.
• Fixed an issue with unmapped Neo4j node ids throwing ArrayIndexOutOfBoundsException.
• Fixed a bug where the in-memory storage engine would not find the correct graph store if the db name was not lowercase
• Fixed a bug where the graph store would be released when storing the CypherGraphStore in the catalog
• Fixed a bug where Node2Vec would produce an ArrayIndexOutOfBounds error on sufficiently large graphs.

Improvements

• Added context information to log entries in debug and warning.
• Log Training loss as part of general progress logging
• Running transactions while projecting a graph now has less chance of breaking the projected graph
• Improve runtime performance for FastRP
• Use Neo4j node id instead of internal GDS node id when seeding generation of initial random vectors in FastRP.
• The in-memory cypher db is now capable of querying relationship ids, types and properties
• The procedure gds.alpha.randomWalk.stream has been improved and should now run faster and more stable.

Graph Data Science 1.8.0-Preview

GDS 1.8 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

• GDS now throws error messages on identifiers with trailing whitespaces to avoid input errors. This affects graphName, modelName, and several property parameters such as nodeWeightProperty or seedProperty.
• We have removed the separate concurrency parameter from the model parameter space in gds.alpha.ml.nodeClassification.train, gds.alpha.ml.linkPrediction.train and gds.alpha.ml.pipeline.linkPrediction.configureParams. The concurrency value in the configuration of the train procedure will be used.
• The procedure gds.alpha.randomWalk.stream has been improved and aligned with the Node2Vec implementation. Please consult the documentation to find out about the new configuration options.
• The procedure gds.beta.fastRPExtended has been merged with gds.fastRP.

New features

• Link Prediction
  • Add new link prediction stream procedure gds.alpha.ml.pipeline.linkPrediction.predict.stream.
  • Added probabilityDistribution and samplingStats to the result of gds.alpha.ml.pipeline.linkPrediction.predict.mutate.
  • To improve prediction performance, we’ve added kNN-based approximate search strategy option to link prediction procedures gds.alpha.ml.pipeline.linkPrediction.predict.stream|mutate.
  • Node property steps in Link Prediction pipelines can use a relationship property.
• Node Classification pipelines: similar to link prediction pipelines, we’ve added a pipeline procedure for node classification, where users can define the features, splitting strategy, and model training options. We’ve added:
  • gds.alpha.ml.pipeline.nodeClassification.create
  • gds.alpha.ml.pipeline.nodeClassification.addNodeProperty
  • gds.alpha.ml.pipeline.nodeClassification.addFeatures
  • gds.alpha.ml.pipeline.nodeClassification.configureParams
  • gds.alpha.ml.pipeline.nodeClassification.configureSplit
  • gds.alpha.ml.pipeline.nodeClassification.train
  • gds.alpha.ml.pipeline.nodeClassification.predict.mutate|stream|write
• New algorithm: Conductance, gds.alpha.conductance.stream, can be used to compute a metric to evaluate the quality of communities identified by community detection algorithms.
• Added support for preserving a relationship property in gds.alpha.ml.splitRelationships.mutate.
• The procedure gds.fastRP has received additional configuration parameters:
  • featureProperties: to configure using node properties as part of the embedding.
  • propertyRatio: to control how much of the embedding is computed from properties.
  • nodeSelfInfluence: allows using each node's initial random vector as a contribution to the node's embedding. Especially useful for graphs with disconnected nodes.

Bug fixes

• Added check that concurrency is meeting determinism constraints for K-Nearest Neighbors whenever randomSeed is overridden.
• Fixed an ArrayIndexOutOfBounds error that could happen in triangle count on some graphs with multiple relationship types.
• Fixed an issue where seeded algorithms (such as WCC) on graphs with multiple node labels could assign seeded communities to new nodes.
• Fixed an issue where KNN did not add candidates to the topK result.
• Fixed an issue where running an algorithm could return incorrect results on graphs filtered with the configuration parameter nodeLabels.
• Fixed an issue where running gds.alpha.ml.pipeline.linkPrediction.train could result in an error on graphs filtered with the configuration parameter nodeLabels.
• Fixed an ArrayIndexOutOfBounds error that could happen in triangle count on some graphs with multiple relationship types.
• Fixed an issue with unmapped Neo4j node ids throwing ArrayIndexOutOfBoundsException.
• Fixed a bug where the in-memory storage engine would not find the correct graph store if the db name was not lowercase
• Fixed a bug where the graph store would be released when storing the CypherGraphStore in the catalog
• Fixed a bug where Node2Vec would produce an ArrayIndexOutOfBounds error on sufficiently large graphs.

Improvements

• Added context information to log entries in debug and warning.
• Log Training loss as part of general progress logging
• Running transactions while projecting a graph now has less chance of breaking the projected graph
• Improve runtime performance for FastRP
• Use Neo4j node id instead of internal GDS node id when seeding generation of initial random vectors in FastRP.
• The in-memory cypher db is now capable of querying relationship ids, types and properties
• The procedure gds.alpha.randomWalk.stream has been improved and should now run faster and more stable.

Graph Data Science 1.7.2

GDS 1.7.2 is compatible with Neo4j 4.1, 4.2, and 4.3 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

Bug fixes

• Fixed an issue where seeded algorithms (such as WCC) on graphs with multiple node labels could assign seeded communities to new nodes.
• Fixed an issue where KNN did not add candidates to the topK result.
• Fixed an issue where running an algorithm could return incorrect results on graphs filtered with the configuration parameter nodeLabels.
• Fixed an issue where running gds.alpha.ml.pipeline.linkPrediction.train could result in an error on graphs filtered with the configuration parameter nodeLabels.
• Fixed an issue with unmapped Neo4j node ids throwing ArrayIndexOutOfBoundsException

GDS 1.1.7

GDS 1.1.7 is compatible with Neo4j Neo4j 3.5.x. For a 4.x compatible release, please see GDS 1.7.2.

Bug fixes

• Fixed a bug in Louvain where changes to maxIterations were ignored.
• Fixed a bug which caused gds.graph.list and gds.graph.drop to throw an error when specifying a graph with duplicate property keys by failing early
• Fixed a bug where gds.alpha.scc would sometimes fail with an ArrayIndexOutOfBoundsException.
• Fixed an issue where running an algorithm could return incorrect results on graphs filtered with the configuration parameter nodeLabels.