Link Prediction With node2vec in Physics Collaboration Network

This page summarizes the projects mentioned and recommended in the original post on dev.to

Judoscale - Save 47% on cloud hosting with autoscaling that just works
Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
judoscale.com
featured
InfluxDB high-performance time series database
Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
influxdata.com
featured
  1. gqlalchemy

    GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder.

    As already mentioned, link prediction refers to the task of predicting missing links or links that are likely to occur in the future. In this tutorial, we will make use the of MAGE spell called node2vec. Also, we will use Memgraph to store data, and gqlalchemy to connect from a Python application. The dataset will be similar to the one used in this paper: Graph Embedding Techniques, Applications, and Performance: A Survey.

  2. Judoscale

    Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.

    Judoscale logo
  3. mage

    MAGE - Memgraph Advanced Graph Extensions :crystal_ball: (by memgraph)

    We will import these 118521 edges, and act as if they are undirected. The Node2Vec algorithm in MAGE accepts parameters whether to treat graph from Memgraph as directed or undirected.

  4. mgconsole

    mgconsole is a command-line interface (CLI) used to interact with Memgraph from any terminal or operating system.

    Note: you can import datasets through one of the querying tools. We have developed our drivers using the Bolt protocol to deliver better performance. You can use Memgraph Lab, mgconsole or even one of our drivers, like the Python driver used in this tutorial.

  5. scikit-learn

    scikit-learn: machine learning in Python

    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts

  • Importing Table Data Into a Graph Database With GQLAlchemy

    3 projects | dev.to | 1 Mar 2023
  • Neo4j vs Memgraph - How to choose a graph database?

    4 projects | dev.to | 8 Dec 2022
  • How to Implement Custom JSON Utility Procedures With Memgraph MAGE and Python

    1 project | dev.to | 4 Jul 2023
  • Optimizing Telco Networks With Graph Coloring & Memgraph MAGE

    1 project | dev.to | 10 May 2023
  • Synchronize Data Between Memgraph Graph Database and Elasticsearch

    1 project | dev.to | 3 Apr 2023