# graph algorithms

Published articles for graph algorithms.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Understanding GraphSAGE

DevFeed: [Understanding GraphSAGE](<https://devfeed.tech/articles/understanding-graphsage-50062.md>)

Original publisher: [Read original article](<https://neo4j.com/blog/aura-graph-analytics/understanding-graphsage/>)

Author: corydon baylor

Published: 2026-08-26T13:00:00Z

Content type: tutorial

Language: en

Sources: [Graph Database & Technology | Neo4j Blog](<https://devfeed.tech/sources/graph-database-technology-neo4j-blog.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [aura-graph-analytics](<https://devfeed.tech/tags/aura-graph-analytics.md>), [auradb](<https://devfeed.tech/tags/auradb.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graph-algorithms](<https://devfeed.tech/tags/graph-algorithms.md>), [graph-data-science](<https://devfeed.tech/tags/graph-data-science.md>), [graph-database](<https://devfeed.tech/tags/graph-database.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neo4j](<https://devfeed.tech/tags/neo4j.md>)

### AI overview

This tutorial explains GraphSAGE, an inductive graph embedding model. It shows how to sample a node's neighbors, aggregate their features, and learn weights that produce embeddings for downstream machine learning tasks.

### Source excerpt

Consider the graph below, which models a movie content platform. You have various users and a few movies and TV shows that users have watched. To keep things simple, there will be only four programs and three users. If you... Read more ->

## Train once, embed forever: GraphSAGE and the model repository in Aura Graph Analytics

DevFeed: [Train once, embed forever: GraphSAGE and the model repository in Aura Graph Analytics](<https://devfeed.tech/articles/train-once-embed-forever-graphsage-and-the-model-repository-in-aura-graph-analytics-50061.md>)

Original publisher: [Read original article](<https://neo4j.com/blog/aura-graph-analytics/train-once-embed-forever-graphsage-and-the-model-repository-in-aura-graph-analytics/>)

Author: corydon baylor

Published: 2026-08-26T13:00:00Z

Content type: tutorial

Language: en

Sources: [Graph Database & Technology | Neo4j Blog](<https://devfeed.tech/sources/graph-database-technology-neo4j-blog.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [aura-graph-analytics](<https://devfeed.tech/tags/aura-graph-analytics.md>), [auradb](<https://devfeed.tech/tags/auradb.md>), [customers](<https://devfeed.tech/tags/customers.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graph-algorithms](<https://devfeed.tech/tags/graph-algorithms.md>), [graph-analytics](<https://devfeed.tech/tags/graph-analytics.md>), [graph-data-science](<https://devfeed.tech/tags/graph-data-science.md>), [model](<https://devfeed.tech/tags/model.md>), [neo4j](<https://devfeed.tech/tags/neo4j.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This tutorial explains how GraphSAGE generates embeddings for previously unseen graph nodes using their features and neighborhood structure. It shows how Aura Graph Analytics can store the trained model so new customers can be embedded without retraining the entire graph.

### Source excerpt

Fifty new customers just signed up. Your recommendation engine needs to place them into your graph's embedding space today, not after tonight's retraining job. This is the cold-start problem, and it's the moment most embedding techniques fail. Classic node embedding... Read more ->

## SumoDB in Neo4j: Microsoft Fabric bridging Decades of Rikishi -- Part 4

DevFeed: [SumoDB in Neo4j: Microsoft Fabric bridging Decades of Rikishi -- Part 4](<https://devfeed.tech/articles/sumodb-in-neo4j-microsoft-fabric-bridging-decades-of-rikishi-part-4-50060.md>)

Original publisher: [Read original article](<https://neo4j.com/blog/aura-graph-analytics/sumodb-in-neo4j-microsoft-fabric-bridging-decades-of-rikishi-part-4/>)

Author: Jocelyn Hoppa

Published: 2026-08-10T16:38:00Z

Content type: article

Language: en

Sources: [Graph Database & Technology | Neo4j Blog](<https://devfeed.tech/sources/graph-database-technology-neo4j-blog.md>)

Topics: [Neo4j](<https://devfeed.tech/topics/neo4j.md>), [data](<https://devfeed.tech/topics/data.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [API](<https://devfeed.tech/topics/api.md>), [JSON](<https://devfeed.tech/topics/json.md>), [Python](<https://devfeed.tech/topics/python.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [api](<https://devfeed.tech/tags/api.md>), [aura-graph-analytics](<https://devfeed.tech/tags/aura-graph-analytics.md>), [data](<https://devfeed.tech/tags/data.md>), [developer](<https://devfeed.tech/tags/developer.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graph-algorithms](<https://devfeed.tech/tags/graph-algorithms.md>), [graph-analytics](<https://devfeed.tech/tags/graph-analytics.md>), [japan](<https://devfeed.tech/tags/japan.md>), [json](<https://devfeed.tech/tags/json.md>), [lakehouse](<https://devfeed.tech/tags/lakehouse.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [microsoft-fabric](<https://devfeed.tech/tags/microsoft-fabric.md>), [neo4j](<https://devfeed.tech/tags/neo4j.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [python](<https://devfeed.tech/tags/python.md>), [sumo](<https://devfeed.tech/tags/sumo.md>)

### AI overview

This fourth part of a Neo4j SumoDB series tests whether sumo wrestling styles have changed over 26 years. It builds a data pipeline in Microsoft Fabric using a Lakehouse, Aura Graph Analytics, graph clustering, and results returned to OneLake.

### Source excerpt

Nostalgia in sports is common -- we often look back on previous generations with gilded lens. Ask any sumo fan whether the sport has changed and you'll get the same answer: the big men don't grapple like they used to. The belt... Read more ->

## GSoC 2018 - Parallel Implementations of Graph Analysis Algorithms

DevFeed: [GSoC 2018 - Parallel Implementations of Graph Analysis Algorithms](<https://devfeed.tech/articles/gsoc-2018-parallel-implementations-of-graph-analysis-algorithms-46293.md>)

Original publisher: [Read original article](<https://julialang.org/blog/2019/02/light-graphs/index.html>)

Author: Soham Tamba

Published: 2019-02-03T00:00:00Z

Content type: article

Language: en

Sources: [JuliaLang - The Julia programming language](<https://devfeed.tech/sources/julialang-the-julia-programming-language.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [parallel](<https://devfeed.tech/topics/parallel.md>), [The Julia Language](<https://devfeed.tech/topics/julia.md>), [Development](<https://devfeed.tech/topics/development.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [breadth-first-search](<https://devfeed.tech/tags/breadth-first-search.md>), [code](<https://devfeed.tech/tags/code.md>), [development](<https://devfeed.tech/tags/development.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graph-algorithms](<https://devfeed.tech/tags/graph-algorithms.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [gsoc](<https://devfeed.tech/tags/gsoc.md>), [io](<https://devfeed.tech/tags/io.md>), [linux](<https://devfeed.tech/tags/linux.md>), [mentor](<https://devfeed.tech/tags/mentor.md>), [np-hard](<https://devfeed.tech/tags/np-hard.md>), [parallel](<https://devfeed.tech/tags/parallel.md>), [projects](<https://devfeed.tech/tags/projects.md>), [vertex-cover](<https://devfeed.tech/tags/vertex-cover.md>)

### AI overview

A summary of a GSoC 2018 project on parallel graph development in the LightGraphs codebase. The project produced parallel and improved sequential implementations of graph algorithms and heuristics for NP-hard graph problems, with benchmarks run on a 64-bit Linux machine using four cores.

### Source excerpt

GSoC 2018 - Parallel Implementations of Graph Analysis Algorithms | This blog briefly summarises my GSoC 2018 project (Parallel Graph Development (https://summerofcode.withgoogle.com/archive/2018/projects/5193483178475520/)) and the results achieved. For a detailed description, please refer to my GSoC blog (https://sohamtamba.github.io/GSoC)....

## January 2015 in the Flink community

DevFeed: [January 2015 in the Flink community](<https://devfeed.tech/articles/january-2015-in-the-flink-community-48265.md>)

Original publisher: [Read original article](<https://flink.apache.org/2015/02/04/january-2015-in-the-flink-community/>)

Published: 2015-02-04T10:00:00Z

Content type: news

Language: en

Sources: [Apache Flink® -- Stateful Computations over Data Streams on Apache Flink](<https://devfeed.tech/sources/apache-flink-stateful-computations-over-data-streams-on-apache-flink.md>)

Topics: [flink](<https://devfeed.tech/topics/flink.md>), [releases](<https://devfeed.tech/topics/releases.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Hadoop](<https://devfeed.tech/topics/hadoop.md>), [Pull Request](<https://devfeed.tech/topics/pull-request.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [contributions](<https://devfeed.tech/tags/contributions.md>), [flink](<https://devfeed.tech/tags/flink.md>), [graph-algorithms](<https://devfeed.tech/tags/graph-algorithms.md>), [hadoop](<https://devfeed.tech/tags/hadoop.md>), [java](<https://devfeed.tech/tags/java.md>), [release](<https://devfeed.tech/tags/release.md>), [release-notes](<https://devfeed.tech/tags/release-notes.md>), [roadmap](<https://devfeed.tech/tags/roadmap.md>)

### AI overview

A summary of January 2015 developments in the Flink community, including the Flink 0.8.0 release, the 2015 roadmap, community presentations, code contributions, Gelly's Graph API, optimizer annotations, and an improved YARN client.

### Source excerpt

Happy 2015! Here is a (hopefully digestible) summary of what happened last month in the Flink community. 0.8.0 release # Flink 0.8.0 was released. See here for the release notes. Flink roadmap # The community has published a roadmap for 2015 on the Flink wiki. Check it out to see what is coming up in Flink, and pick up an issue to contribute! Articles in the press # The Apache Software Foundation announced Flink as a Top-Level Project.