link-prediction | Predict links in a citation network | Machine Learning library

 by   raph-m Python Version: Current License: MIT

X-RayKey FeaturesCode SnippetsCommunity Discussions(2)VulnerabilitiesInstallSupport

kandi X-RAY | link-prediction Summary

kandi X-RAY | link-prediction Summary

link-prediction is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning applications. link-prediction has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However link-prediction build file is not available. You can download it from GitHub.

Predict links in a citation network
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              link-prediction has a low active ecosystem.
              It has 10 star(s) with 2 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              link-prediction has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of link-prediction is current.

            kandi-Quality Quality

              link-prediction has 0 bugs and 0 code smells.

            kandi-Security Security

              link-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              link-prediction code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              link-prediction is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              link-prediction releases are not available. You will need to build from source code and install.
              link-prediction has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed link-prediction and discovered the below as its top functions. This is intended to give you an instant insight into link-prediction implemented functionality, and help decide if they suit your requirements.
            • Plot a grid plot
            • Returns a list of ticker labels
            • Generate a list of ticks for a double ticker
            • Optimized objective function
            • Compute f1 score
            • Calculates the cosine distance between two IDs
            • Return tfidf encoding
            • Load training data
            • Plot the importances of a forest
            • Compute the score between two terms
            Get all kandi verified functions for this library.

            link-prediction Key Features

            No Key Features are available at this moment for link-prediction.

            link-prediction Examples and Code Snippets

            No Code Snippets are available at this moment for link-prediction.

            Community Discussions

            Trending Discussions on link-prediction

            Linkprediction using Hinsage/Graphsage in StellarGraph returns NaNs
            Stellargraph and Node2Vec embedding

            QUESTION

            Linkprediction using Hinsage/Graphsage in StellarGraph returns NaNs
            Asked 2021-Dec-01 at 22:58

            I am trying to run a link prediction using HinSAGE in the stellargraph python package.

            I have a network of people and products, with edges from person to person (KNOWs) and person to products (BOUGHT). Both people and products got a property vector attached, albeit a different one from each type (Persons vector is 1024 products is 200). I am trying to create a link prediction algorithm from person to product based on all the information in the network. The reason for me for using HinSAGE is the option for inductive learning.

            I have the code below, and I thought I was doing it similar to the examples

            https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/graphsage-link-prediction.html

            but I keep getting "nan" as my output predictions, anyone got a suggestion to what I can try?

            ...

            ANSWER

            Answered 2021-Nov-04 at 14:43

            So I found the problem, might be useful for others. If there is any node containing missing data, the thing will just produce NAs. Especially dangerous if you create your graph by joining pandas dataframes, I had a typo in one file that was integrated and led to the problem.

            Source https://stackoverflow.com/questions/69728856

            QUESTION

            Stellargraph and Node2Vec embedding
            Asked 2021-Oct-06 at 13:55

            I'm trying to do a link prediction with stellargraph, following the documention tutorial.
            When I reach this part :

            ...

            ANSWER

            Answered 2021-Oct-06 at 13:55

            I finally found the solution. It was quite unclear (at least to me) from the documentation but your nodes' labels must be string and not integer. So a simple .astype(str) in my dataframe fixed it. I hope this will help others in the future !

            Source https://stackoverflow.com/questions/69434060

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install link-prediction

            You can download it from GitHub.
            You can use link-prediction like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Reuse Trending Solutions

            build-a-realtime-voice-to-image-generator-using-generative-ai
            build-a-realtime-voice-to-image-generator-using-generative-ai
            Build a Realtime Voice-to-Image Generator using Generative AI
            image-resizing-using-opencv-in-python
            image-resizing-using-opencv-in-python
            Image Resizing using OpenCV in Python
            build-your-own-gpt4all-content-generator
            build-your-own-gpt4all-content-generator
            Build your own Custom GPT Content Generator (Open-Source ChatGPT Alternative)
            validate-an-email-address-in-javascript
            validate-an-email-address-in-javascript
            How to Validate an Email Address in JavaScript
            age-calculator-using-javascript
            age-calculator-using-javascript
            Age Calculator using JavaScript
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            Addressing Bias in AI - Toolkit for Fairness, Explainability and Privacy
            javascript-node-js-payment-processing
            javascript-node-js-payment-processing
            15 best JavaScript Node.js Payment libraries
            build-credit-risk-predictor-using-federated-learning
            build-credit-risk-predictor-using-federated-learning
            Build Credit Risk predictor using Federated Learning
            top-10-javascript-tours-and-guides-libraries-in-2023
            top-10-javascript-tours-and-guides-libraries-in-2023
            10 Best JavaScript Tours and Guides Libraries in 2023
            disease-predictor
            disease-predictor
            Disease Predictor using Pandas & Scikit
            python-face-recognition
            python-face-recognition
            28 best Python Face Recognition libraries

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/raph-m/link-prediction.git

          • CLI

            gh repo clone raph-m/link-prediction

          • sshUrl

            git@github.com:raph-m/link-prediction.git

            • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Advertising and MarketingTelecommunications and MediaArtificial IntelligenceMachine Learning

            Reuse Machine Learning Kits

            Stop words : NLP
            9 best JavaScript Machine Learning libraries
            9 best Java Machine Learning libraries
            8 best Go Machine Learning libraries
            7 best C++ Machine Learning libraries
            See all related Kits

            Reuse Artificial Intelligence Kits

            Generative AI for Art
            19 best Python Computer Vision libraries
            5 best Java Automation libraries
            9 best Go Automation libraries
            5 best PHP Automation libraries
            See all related Kits

            Consider Popular Machine Learning Libraries

            tensorflow

            by tensorflow

            youtube-dl

            by ytdl-org

            models

            by tensorflow

            pytorch

            by pytorch

            keras

            by keras-team

            See all Machine Learning Libraries

            Try Top Libraries by raph-m

            DNA-alignment

            by raph-mJava

            football-championship

            by raph-mPython

            author-prediction-full-project

            by raph-mC++

            author_classification

            by raph-mC++

            exposure_fusion

            by raph-mJupyter Notebook

            See all Learning Libraries

            Open Weaver – Develop Applications Faster with Open Source

            kandi

            Community and Support

            Company

            Follow

            • © 2023 Open Weaver Inc.