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.
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.
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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.
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.
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