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.

GDS 1.7.1

Release Date October 12, 2021

GDS 1.7.1 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 1.1.6. For a 4.0 compatible release, please see GDS 1.6.5

• Fixed a bug where Cypher graph loading and subgraph creation which could lead to ArrayIndexOutOfBounds errors.
• Fixed an ArrayIndexOutOfBounds caused by running triangleCount on graphs with multiple relationship types.

Graph Data Science 1.7.0

GDS 1.7.0 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 1.1.6. For a 4.0 compatible release, please see GDS 1.6.5

Breaking changes

• This release does not support Neo4j 4.0.x
• Align returned modelInfo entry names of gds.alpha.ml.linkPrediction.train and gds.alpha.ml.nodeClassification.train with the model catalog. Now containing modelName and modelInfo instead of name and info.
• Remove the sharedUpdater parameter from gds.alpha.ml.linkPrediction and gds.alpha.ml.nodeClassification.
• gds.beta.graph.export.csv now exports into a subdirectory called export. Previously, the exported graphs were written directly into the configured directory.
• Renamed all graphalgo packages to gds

New features

• New Algorithm: Approximate Maximum K-Cut
  • Includes procedures: gds.alpha.maxkcut.[mutate|mutate.estimate|stream|stream.estimate].
• Introduced Link Prediction Pipelines to make it easier to define and calculate features, split your graph, and make predictions.
  • Includes procedures: gds.alpha.ml.pipeline.linkPrediction.create|addNodeProperty|addFeature|configureSplit|configureParams|train|predict.mutate.
• Introduced support for exporting additional node properties, including strings, from the underlying database.
  • Added additionalNodeProperties parameter to gds.graph.export
  • Added additionalNodeProperties parameter to gds.graph.export.csv
• Introduced experimental support for querying the in-memory graph with Cypher
  • Added gds.alpha.create.cypherdb to allow neo4j to recognize the in-memory graph as a database for Cypher queries
• To allow users better ability to handle multiple concurrent users, we’ve added a system monitoring procedure, gds.alpha.systemMonitor, to provide an overview of the system's workload and available resources.
• Progress logging is now turned on by default, and no longer requires changing your configuration settings. Progress can be accessed with gds.beta.listProgress
• GraphSAGE now supports deterministic results with the randomSeed configuration parameter to gds.beta.graphSage.train.
• Improve performance (up to 20x speedup) of weakly connected components, gds.wcc, for undirected graphs by applying a subgraph sampling optimization.

Bug fixes

• Fixed a bug regarding weighted graphs with multiple relationship types, which affected gds.beta.graphSage and gds.alpha.spanningTree.
• Supervised Machine Learning (Node Classification & Link Prediction):
  • Fixed a NaN issue in NodeClassification where computations with very small probability values can cause the result to flip to infinity.
  • Fixed a bug in seeded NodeClassification and LinkPrediction which lead to non-deterministic behaviour.
  • Corrected the training size used in gds.alpha.ml.linkPrediction.train. This affects the penality parameter used in logistic regression.
• Progress Logging:
  • Fixed a bug in beta progress event tracking where progress events would not be released if computation was abandoned before completion.
  • Fixed a bug in beta progress event tracking for Pregel algorithms where progress events would not be released on algorithm completion.
• Node Similarity & KNN:
  • Fixed a bug where on a node-filtered multi-relationship-type graph KNN and NodeSimilarity could write out of bounds.
  • Fixed a bug which affected gds.nodeSimilarity.write and gds.alpha.knn.write when being executed in combination with a nodeLabels filter. The bug either led to an exception or to wrong results due to an incorrect mapping between internal and Neo4j node ids.
  • Fixed a bug where gds.nodeSimilarity.[write|mutate] and gds.beta.knn.[write|mutate] wrote duplicate relationships if the input graph is undirected.
• KNN:
  • Fixed a bug in gds.beta.knn where negative values in node properties of type float arrays failed when returning the similarityDistribution.
• Fast RP:
  • FastRP stream mode explicitly returns a list of floats rather than a list of numbers. This agrees with the other embeddings, and saves users from having to cast/transform when processing the results further in Cypher.
• GraphSAGE:
  • Fixed a bug in weighted GraphSAGE where the relationshipWeightProperty was not loaded.
  • Fixed a bug in gds.beta.graphSage, where the concurrency parameter was not considered.
• Graph Operations:
  • Fixed a bug in gds.graph.removeNodeProperties where removedPropertiesWritten was too large for properties shared across multiple labels.
  • Fixed a bug in gds.beta.graph.generate, where random graphs with relationship properties could not be generated.
  • Fixed a bug in gds.create.subgraph which could lead to undefined behaviour or an AIOOB exception when executed on GDS Enterprise Edition.
  • Fixed a bug in gds.graph.create, where default values for array properties would throw for convertable types.

  Improvements

  • Pathfinding: Added existence checks for sourceNode and targetNode to all shortest path procedures in the product tier.
  • Improved runtime of gds.fastRP via better workload balancing between threads.
  • Lower memory footprint for LinkPrediction and NodeClassification.
  • Improved the procedure output of gds.beta.listProgress.
  • Scale down scores computed by gds.articleRank.

Graph Data Science 1.6.5

GDS 1.6.5 is compatible with Neo4j 4.0, 4.1, 4.2, and 4.3 but not Neo4j 3.5.x. For a 3.5 compatible release, please see GDS 1.1.6.

Bug fixes

• Fixed a bug in gds.beta.graph.generate, where random graphs with relationship properties could not be generated.
• Fixed a bug in gds.graph.create, where default values for array properties would throw for convertable types.
• Fixed a bug in gds.beta.graphSage, where the concurrency parameter was not considered.
• Fixed a bug where the BitIdMap node mapping builder (on by default in GDS Enterprise Edition) would not correctly count all nodes in certain situations.
• Corrected the training size used in gds.alpha.ml.linkPrediction.train. This affects the penality parameter used in logistic regression.

GDS 1.7.0-Preview

GDS 1.7.0-preview 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 1.1.6. For a 4.0 compatible release, please see GDS 1.1.6

Breaking changes

• This release does not support Neo4j 4.0.x
• Align returned modelInfo entry names of gds.alpha.ml.linkPrediction.train and gds.alpha.ml.nodeClassification.train with the model catalog. Now containing modelName and modelInfo instead of name and info.
• Remove the sharedUpdater parameter from gds.alpha.ml.linkPrediction and gds.alpha.ml.nodeClassification.
• gds.beta.graph.export.csv now exports into a subdirectory called export. Previously, the exported graphs were written directly into the configured directory.
• Renamed all graphalgo packages to gds

New features

• New Algorithm: Approximate Maximum K-Cut
  • Includes procedures: gds.alpha.maxkcut.[mutate|mutate.estimate|stream|stream.estimate].
• Introduced Link Prediction Pipelines to make it easier to define and calculate features, split your graph, and make predictions.
  • Includes procedures: gds.alpha.ml.pipeline.linkPrediction.create|addNodeProperty|addFeature|configureSplit|configureParams|train|predict.mutate.
• Introduced support for exporting additional node properties, including strings, from the underlying database.
  • Added additionalNodeProperties parameter to gds.graph.export
  • Added additionalNodeProperties parameter to gds.graph.export.csv
• Introduced experimental support for querying the in-memory graph with Cypher
  • Added gds.alpha.create.cypherdb to allow neo4j to recognize the in-memory graph as a database for Cypher queries
• To allow users better ability to handle multiple concurrent users, we’ve added a system monitoring procedure, gds.alpha.systemMonitor, to provide an overview of the system's workload and available resources.
• Progress logging is now turned on by default, and no longer requires changing your configuration settings. Progress can be accessed with gds.beta.listProgress
• GraphSAGE now supports deterministic results with the randomSeed configuration parameter to gds.beta.graphSage.train.
• Improve performance (up to 20x speedup) of weakly connected components, gds.wcc, for undirected graphs by applying a subgraph sampling optimization.

Bug fixes

• Fixed a bug regarding weighted graphs with multiple relationship types, which affected gds.beta.graphSage and gds.alpha.spanningTree.
• Supervised Machine Learning (Node Classification & Link Prediction):
  • Fixed a NaN issue in NodeClassification where computations with very small probability values can cause the result to flip to infinity.
  • Fixed a bug in seeded NodeClassification and LinkPrediction which lead to non-deterministic behaviour.
  • Corrected the training size used in gds.alpha.ml.linkPrediction.train. This affects the penality parameter used in logistic regression.
• Progress Logging:
  • Fixed a bug in beta progress event tracking where progress events would not be released if computation was abandoned before completion.
  • Fixed a bug in beta progress event tracking for Pregel algorithms where progress events would not be released on algorithm completion.
• Node Similarity & KNN:
  • Fixed a bug where on a node-filtered multi-relationship-type graph KNN and NodeSimilarity could write out of bounds.
  • Fixed a bug which affected gds.nodeSimilarity.write and gds.alpha.knn.write when being executed in combination with a nodeLabels filter. The bug either led to an exception or to wrong results due to an incorrect mapping between internal and Neo4j node ids.
  • Fixed a bug where gds.nodeSimilarity.[write|mutate] and gds.beta.knn.[write|mutate] wrote duplicate relationships if the input graph is undirected.
• KNN:
  • Fixed a bug in gds.beta.knn where negative values in node properties of type float arrays failed when returning the similarityDistribution.
• Fast RP:
  • FastRP stream mode explicitly returns a list of floats rather than a list of numbers. This agrees with the other embeddings, and saves users from having to cast/transform when processing the results further in Cypher.
• GraphSAGE:
  • Fixed a bug in weighted GraphSAGE where the relationshipWeightProperty was not loaded.
  • Fixed a bug in gds.beta.graphSage, where the concurrency parameter was not considered.
• Graph Operations:
  • Fixed a bug in gds.graph.removeNodeProperties where removedPropertiesWritten was too large for properties shared across multiple labels.
  • Fixed a bug in gds.beta.graph.generate, where random graphs with relationship properties could not be generated.
  • Fixed a bug in gds.create.subgraph which could lead to undefined behaviour or an AIOOB exception when executed on GDS Enterprise Edition.
  • Fixed a bug in gds.graph.create, where default values for array properties would throw for convertable types.

  Improvements

  • Pathfinding: Added existence checks for sourceNode and targetNode to all shortest path procedures in the product tier.
  • Improved runtime of gds.fastRP via better workload balancing between threads.
  • Lower memory footprint for LinkPrediction and NodeClassification.
  • Improved the procedure output of gds.beta.listProgress.
  • Scale down scores computed by gds.articleRank.

GDS 1.6.4

Release date: July 29, 2021

Bug fixes

• Fixed a bug in gds.create.subgraph which could lead to undefined behaviour or an AIOOB exception when executed on GDS Enterprise Edition.

GDS 1.6.3

Release Date: July 22, 2021

Breaking changes

• Remove the sharedUpdater parameter from gds.alpha.ml.linkPrediction and gds.alpha.ml.nodeClassification.

New features

Bug fixes

• Fixed a bug which affected gds.nodeSimilarity.write and gds.alpha.knn.write when being executed in combination with a nodeLabels filter.
  The bug either led to an exception or to wrong results due to an incorrect mapping between internal and Neo4j node ids.
• Fixed a bug in seeded NodeClassification and LinkPrediction which lead to non-deterministic behaviour.
• Fixed a bug where gds.nodeSimilarity.[write|mutate] and gds.beta.knn.[write|mutate] wrote duplicate relationships if the input graph is undirected.
• Fixed a bug in gds.beta.knn where negative values in node properties of type float arrays failed when returning the similarityDistribution.

Improvements

• Lower memory footprint for LinkPrediction and NodeClassification.

GDS 1.6.2

Release Date: July 8, 2021

GDS 1.6.2 is compatible with Neo4j 4.3, 4.2, 4.1, and 4.0. It is not compatible with Neo4j 3.5.x - for a compatible release, please see GDS 1.1.6

Bug Fixes

• Fixed a bug in weighted GraphSAGE where the relationshipWeightProperty was not loaded.
• Fixed a bug in gds.beta.node2vec where using relationship weights would not work when running concurrently.
• Fixed a bug in gds.graph.create where the default value could not be changed for array properties.

1.6.1

Release Date: 17 June 2021

GDS 1.6.1 is compatible with Neo4j 4.0, 4.1, 4.2, and 4.3 but not Neo4j 3.5.x. For a 3.5 compatible release, please see GDS 1.1.6.

Bug fixes

• Fixed a bug regarding weighted graphs with multiple relationship types, which affected gds.beta.graphSage and gds.alpha.spanningTree.
• Fix NaN issue in NodeClassification where computations with very small probability values can cause the result to flip to infinity.
• Progress logging (gds.beta.listProgress)
  • Fixed a bug where progress events would not be released if computation was abandoned before completion.
  • Fixed a bug with Pregel algorithms logging where progress events would not be released on algorithm completion.
• Fixed a bug regarding mutated node properties that could cause an AIOOB exception.
• Fixed a bug where on a node-filtered multi-relationship-type graph KNN and NodeSimilarity could write out of bounds.
• FastRP stream mode explicitly returns a list of floats rather than a list of numbers. This agrees with the other embeddings, and saves users from having to cast/transform when processing the results further in Cypher.

GDS 1.6.0

Release Date: 27 May 2021

GDS 1.6 is compatible with Neo4j 4.0, 4.1, and 4.2 but not Neo4j 3.5.x. For a 3.5 compatible release, please see GDS 1.1.6.

Breaking changes

• Degree centrality has been promoted to the product tier
  • Added procedures:
    • gds.degree.stream.estimate
    • gds.degree.write.estimate
    • gds.degree.mutate
    • gds.degree.mutate.estimate
    • gds.degree.stats
    • gds.degree.stats.estimate
  • Removed alpha procedures:
    • gds.alpha.degree.stream
    • Gds.alpha.degree.write
• Article Rank has been promoted to the product tier
  • Added procedures:
    • gds.articleRank.stream
    • gds.articleRank.stream.estimate
    • gds.articleRank.write
    • gds.articleRank.write.estimate
    • gds.articleRank.mutate
    • gds.articleRank.mutate.estimate
    • gds.articleRank.stats
    • gds.articleRank.stats.estimate
  • Removed alpha procedures:
    • gds.alpha.articleRank.stream
    • gds.alpha.articleRank.write
• Eigenvector Centrality has been promoted to the product tier
  • Added procedures:
    • gds.eigenvector.stream
    • gds.eigenvector.stream.estimate
    • gds.eigenvector.write
    • gds.eigenvector.write.estimate
    • gds.eigenvector.mutate
    • gds.eigenvector.mutate.estimate
    • gds.eigenvector.stats
    • gds.eigenvector.stats.estimate
  • Removed alpha procedures:
    • gds.alpha.eigenvector.stream
    • Gds.alpha.eigenvector.write
• AStar has been promoted to the product tier
  • Added procedures:
    • gds.astar.stream
    • gds.astar.stream.estimate
    • gds.astar.write
    • gds.astar.write.estimate
    • gds.astar.mutate
    • gds.astar.mutate.estimate
  • Removed alpha procedures:
    • gds.beta.astar.stream
    • gds.beta.astar.stream.estimate
    • gds.beta.astar.write
    • gds.beta.astar.write.estimate
    • gds.beta.astar.mutate
    • gds.beta.astar.mutate.estimate
  • The parameter path was removed. The path computation is controlled by the YIELD.
• Yens K Shortest Paths has been promoted to the product tier:
  • Added procedures:
    • gds.yens.stream
    • gds.yens.stream.estimate
    • gds.yens.write
    • gds.yens.write.estimate
    • gds.yens.mutate
    • gds.yens.mutate.estimate
  • Removed alpha procedures:
    • gds.beta.yens.stream
    • gds.beta.yens.stream.estimate
    • gds.beta.yens.write
    • gds.beta.yens.write.estimate
    • gds.beta.yens.mutate
    • gds.beta.yens.mutate.estimate
  • The parameter path was removed. The path computation is controlled by the cypher YIELD sub-clause.
• Dijkstra Source-Target has been promoted to the product tier:
  • Added procedures:
    • gds.shortestPath.dijkstra.stream
    • gds.shortestPath.dijkstra.stream.estimate
    • gds.shortestPath.dijkstra.write
    • gds.shortestPath.dijkstra.write.estimate
    • gds.shortestPath.dijkstra.mutate
    • gds.shortestPath.dijkstra.mutate.estimate
  • Removed alpha procedures:
    • gds.beta.shortestPath.dijkstra.stream
    • gds.beta.shortestPath.dijkstra.stream.estimate
    • gds.beta.shortestPath.dijkstra.write
    • gds.beta.shortestPath.dijkstra.write.estimate
    • gds.beta.shortestPath.dijkstra.mutate
    • gds.beta.shortestPath.dijkstra.mutate.estimate
  • The parameter path was removed. The path computation is controlled by the cypher YIELD sub-clause.
• Dijkstra Single-Source has been promoted to the product tier:
  • Added procedures:
    • gds.allShortestPath.dijkstra.stream
    • gds.allShortestPath.dijkstra.stream.estimate
    • gds.allShortestPath.dijkstra.write
    • gds.allShortestPath.dijkstra.write.estimate
    • gds.allShortestPath.dijkstra.mutate
    • gds.allShortestPath.dijkstra.mutate.estimate
  • Removed alpha procedures:
    • gds.beta.allShortestPath.dijkstra.stream
    • gds.beta.allShortestPath.dijkstra.stream.estimate
    • gds.beta.allShortestPath.dijkstra.write
    • gds.beta.allShortestPath.dijkstra.write.estimate
    • gds.beta.allShortestPath.dijkstra.mutate
    • gds.beta.allShortestPath.dijkstra.mutate.estimate
  • The parameter path was removed. The path computation is controlled by the cypher YIELD sub-clause.
• Node2Vec has been promoted to the beta tier
  • Added procedures:
    • gds.beta.node2vec.stream
    • gds.beta.node2vec.stream.estimate
    • gds.beta.node2vec.write
    • gds.beta.node2vec.write.estimate
    • gds.beta.node2vec.mutate
    • gds.beta.node2vec.mutate.estimate
  • Removed alpha procedures:
    • gds.alpha.node2vec.stream
    • gds.alpha.node2vec.write
• The parameter centerSamplingFactor is renamed to positiveSamplingFactor
• The parameter contextSamplingExponent is renamed to negativeSamplingExponent

The model catalog list feature no longer throws an error when a non-existent model name is given

Node Classification and Link Prediction”

• maxStreakCount configuration parameter is renamed to patience. It is used in the train modes of Node Classification and Link Prediction.
• maxIterations and minIterations configuration parameters are renamed to maxEpochs and minEpochs. It is used in the train modes of Node Classification and Link Prediction.
• windowSize configuration parameters is removed from the train modes of Node Classification and Link Prediction.

gds.alpha.ml.linkPrediction.train configuration parameter classRatio is renamed to negativeClassWeight. It is also mandatory now.

Removed degreeAsProperty configuration parameter from GraphSAGE

• The same effect can be achieved by using gds.degree.mutate and use the mutated property as feature for GraphSAGE training.
• Important: GraphSAGE models persisted with earlier versions of GDS are not compatible with this version.

New features

• New ScaleProperties procedures to transform and scale node properties. Available scalers: Min-max, Max, Mean, Log, Standard Score, L1 Norm, L2 Norm
  • gds.alpha.scaleProperties.stream
  • gds.alpha.scaleProperties.mutate
• Added ability to create new in-memory graphs by filtering existing named graphs based on node and relationship properties with new catalog procedure gds.beta.graph.create.subgraph
• Two new centrality algorithms for influence maximization were contributed by community member @xkitsios
  • gds.alpha.influenceMaximization.celf.stream
  • gds.alpha.influenceMaximization.greedy.stream
• Link Prediction:
  • Added support for storing, loading and publishing Link Prediction models.
  • Added progress logging for gds.alpha.ml.linkPrediction.train and gds.alpha.ml.linkPrediction.predict.
  • Added write and stream modes to gds.alpha.ml.linkPrediction.predict
    • gds.alpha.ml.linkPrediction.stream
    • gds.alpha.ml.linkPrediction.write
  • Added estimate mode for Link Prediction:
    • gds.alpha.ml.linkPrediction.train.estimate
    • gds.alpha.ml.lin...