Showing 2 open source projects for "link structure"

View related business solutions
  • The only CRM built for B2C Icon
    The only CRM built for B2C

    Stop chasing transactions. Klaviyo turns customers into diehard fans—obsessed with your products, devoted to your brand, fueling your growth.

    Klaviyo unifies your customer profiles by capturing every event, and then lets you orchestrate your email marketing, SMS marketing, push notifications, WhatsApp, and RCS campaigns in one place. Klaviyo AI helps you build audiences, write copy, and optimize — so you can always send the right message at the right time, automatically. With real-time attribution and insights, you'll be able to make smarter, faster decisions that drive ROI.
    Learn More
  • Build innovative business apps powered by process automation Icon
    Build innovative business apps powered by process automation

    Connect workflows, teams and systems within one digital business transformation platform

    Manage your business as a unified system of interacting processes. Use BPMN 2.0 for low-code process modeling by business people. Follow your strategic goals with process architecture that always corresponds to the structure of an actual business.
    Learn More
  • 1
    Projects-Solutions

    Projects-Solutions

    Links to others' solutions to Projects

    Projects-Solutions is a companion repository to Projects; while Projects contains the list of project ideas, Projects-Solutions links to solutions submitted by other users in multiple programming languages. It is effectively a community-driven gallery of implementations of the tasks defined in the “Projects” list, allowing learners to compare their solutions, study others’ code, and improve their approach. The repository aggregates links and resources rather than necessarily hosting all code...
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    node2vec

    node2vec

    Learn continuous vector embeddings for nodes in a graph using biased R

    ...The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. ...
    Downloads: 2 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB