GraphSage vs Pinsage #InsideArangoDB
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The ArangoML Group had a detailed discussion on the topic "GraphSage Vs PinSage" where they shared their thoughts on the difference between the working principles of two popular Graph ML algorithms. The following slidedeck is an accumulation of their thoughts about the comparison between the two algorithms.
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Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
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What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...
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Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
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Maxon Cinema 4D 2025 is the latest version of the Maxon's 3D software, released in September 2024, and it builds upon previous versions with new tools for procedural modeling and animation, as well as enhancements to particle, Pyro, and rigid body simulations. CG Channel also mentions that Cinema 4D 2025.2, released in April 2025, focuses on spline tools and unified simulation enhancements.
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GraphSage vs Pinsage #InsideArangoDB
- 1. GraphSage Vs PinSage Discussion between the two popular Graph ML algorithms ArangoDB ML Reading Group
- 2. PinSage ● It is an Inductive based Graph Convolutional Neural Networks (GCNs) for Web-Scale Recommender Systems ● PinSage is a random-walk based GCNs algorithm which learns embeddings for nodes (in billions) in web scale graphs ● Due to its inductive nature it is highly-scalable and generic model. ● Altogether, the Pinterest graph (Dataset) contains 2 billion pins, 1 billion boards, and over 18 billion edges (i.e. memberships of pins to their corresponding boards) ● Once embeddings are learned (aka pin embeddings) it can be used for classification, clustering or reranking. ● It is mainly used by Pinterest for visual recommendations (pins are visual bookmarks e.g. for buying clothes or other products) ● It solves the problem of operating on entire graph laplacian during training.
- 3. PinSage ● Pinterest is a platform in which they share and organise images. ● Images are referred to as Pins ● Users stack similar images in albums (boards) ● PinSage simplify the graph, by forming a Bipartite Graph of pins — boards Reference: PinSage Fig1: PinSage
- 4. GraphSage ● An inductive variant of GCNs ● Could be Supervised or Unsupervised or Semi-Supervised ● Aggregator gathers all of the sampled neighbourhood information into 1-D vector representations ● Does not perform on-the-fly convolutions ● The whole graph needs to be stored in GPU memory ● Does not support MapReduce Inference ● Perform random sampling to get the neighbourhood of a node u PinSage ● Built on top of GraphSage ● Supervised ● It has Convolutions which are same as aggregators in GraphSage ● It performs on-the-fly convolutions (sampling the neighbourhood of nodes on demand) ● The whole graph does not need to be in memory (using producer consumer architecture) ● Supports Efficient MapReduce Inference (while performing aggregation we might compute convolutions repeatedly which can be minimized by mapreduce) ● Use Random Walks for neighborhood sampling (where the neighborhood of a node u is defined as the T nodes that exert the most influence on node u)
- 5. GraphSage ● Does not support importance pooling ● Does not support Curriculum training PinSage ● Supports importance pooling where we compute scores of each and every neighbour using random walk ● Supports Curriculum training where algorithm is fed harder-and-harder examples during training (12% performance gain)