Oracle International Corporation

Damien Hilloulin

Sungpack Hong

Hassan Chafi

Oskar Van Rest

Davide Bartolini

Vlad Ioan Haprian

Vasileios Trigonakis

Alexander Weld

Arnaud Delamare

Jinha Kim

Rhicheek Patra

Yahya Ez-Zainabi

Jonas Schweizer

Bence Czipo

Lukas Kapp-Schwoerer

A sampling procedure is performed for paths on a multi-hop distributed graph that includes vertices partitioned on a plurality of machines; a sampled path includes first and second vertices hosted on first and second machines respectively. The sampling procedure includes communicating, by the first machine to the second machine, first path information comprising an identifier for the second vertex and an identifier for a target vertex of the sampled path; the target vertex is hosted on a target host machine. The procedure further includes communicating edge information by the first machine to the target host machine, and communicating feature information by the second machine to the target host machine. Communication of the edge information and communication of the feature information are deferred relative to communication of the first path information.

Distributed multi-hop neighborhood extraction for graph machine-learning via push-lazy-push-traversal

Techniques are provided herein for efficient representation of heterogeneous graphs in memory. In an embodiment, vertices and edges of the graph are segregated by type. Each property of a type of vertex or edge has values stored in a respective vector. Directed or undirected edges of a same type are stored in compressed sparse row (CSR) format. The CSR format is more or less repeated for edge traversal in either forward or reverse direction. An edge map translates edge offsets obtained from traversal in the reverse direction for use with data structures that expect edge offsets in the forward direction. Subsequent filtration and/or traversal by type or property of vertex or edge entails minimal data access and maximal data locality, thereby increasing efficient use of the graph.

Efficient, in-memory, relational representation for heterogeneous graphs

Efficiently implemented herein is a deterministic semantic for property updates by graph queries. Mechanisms of determinism herein ensure data consistency for graph mutation. These mechanisms facilitate optimistic execution of graph access despite a potential data access conflict. This approach may include various combinations of special activities such as detecting potential conflicts during query compile time, applying query transformations to eliminate those conflicts during code generation where possible, and executing updates in an optimistic way that safely fails if determinism cannot be guaranteed. In an embodiment, a computer receives a request to modify a graph. The request to modify the graph is optimistically executed after preparation and according to safety precautions as presented herein. Based on optimistically executing the request, a data access conflict actually occurs and is automatically detected. Based on the data access conflict, optimistically executing the request is prematurely and automatically halted without finishing executing the request.

Deterministic semantic for graph property update queries and its efficient implementation

Techniques are described herein for learning property graph representations edge-by-edge. In an embodiment, an input graph is received. The input graph comprises a plurality of vertices and a plurality of edges. Each vertex of the plurality of vertices is associated with vertex properties of the respective vertex. A vertex-to-property mapping is generated for each vertex of the plurality of vertices. The mapping maps each vertex to a vertex-property signature of a plurality of vertex-property signatures. A plurality of edge words is generated. Each edge word corresponds to one or more edges that each begin at a first vertex having a particular vertex-property signature of the plurality of vertex property signatures and end at a second vertex having a particular vertex-property signature of the plurality of vertex property signatures. A plurality of sentences is generated. Each sentence comprises edge words directly connected along a path of a plurality of paths in the input graph. Using the plurality of sentences and the plurality of edge words, a document vectorization model is used to generate machine learning vectors that represent the input graph.

Learning property graph representations edge-by-edge

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Zurich, Switzerland

Damien Hilloulin