Representing graph edges using neural networks
US-11455512-B1 · Sep 27, 2022 · US
Deep neural network system for similarity-based graph representations
US2023134742A1 · US · A1
| Field | Value |
|---|---|
| Publication number | US-2023134742-A1 |
| Application number | US-202218087704-A |
| Country | US |
| Kind code | A1 |
| Filing date | Dec 22, 2022 |
| Priority date | May 18, 2018 |
| Publication date | May 4, 2023 |
| Grant date | — |
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- Title
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Abstract
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There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph. The neural network system further comprises one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph; generate a vector representation of the second graph; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.
First claim
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What is claimed is: 1 . A neural network system implemented by one or more computers for determining graph similarity, the neural network system comprising: one or more neural networks configured to: process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state representation vectors and the edge representation vectors to generate a vector representation of the input graph; one or more processors configured to: receive a first graph; receive a second graph; generate a vector representation of the first graph using the one or more neural networks; generate a vector representation of the second graph using the one or more neural networks; determine a similarity score for the first graph and the second graph based upon the vector representations of the first graph and the second graph.
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Classifications
Learning methods · CPC title
characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title
Auto-encoder networks; Encoder-decoder networks · CPC title
Supervised learning · CPC title
- G06F21/563Primary
by source code analysis · CPC title
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Frequently asked questions
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- What does patent US2023134742A1 cover?
- There is described a neural network system implemented by one or more computers for determining graph similarity. The neural network system comprises one or more neural networks configured to process an input graph to generate a node state representation vector for each node of the input graph and an edge representation vector for each edge of the input graph; and process the node state represe…
- Who is the assignee on this patent?
- Deepmind Tech Ltd
- What technology area does this patent fall under?
- Primary CPC classification G06F21/563. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Thu May 04 2023 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 4 related publications on this page (citations in our corpus or others sharing the same primary CPC).