Link Prediction On Heterogeneous Graphs With Pyg Medium

Crandi Man

-Oct 18, 2025, 10:21 AM

link prediction on heterogeneous graphs with pyg medium

Pytorch Geometric Implementations on major graph problems Graph Neural Networks is a machine learning algorithm designed for graph-structured data such as social graphs, networks in cybersecurity, or molecular representations. It has evolved rapidly over the last few years and is used in many different applications. In this blog post, we will review its code implementations on major graph problems along with all the basics of GNN including its applications and algorithm details. GNN can be used to solve a variety of graph-related machine learning problems: Node Classification Predicting the classes or labels of nodes.

For example, detecting fraudulent entities in the network in cybersecurity can be a node classification problem. Link Prediction Predicting if there are potential linkages (edges) between nodes. For example, a social networking service suggests possible friend connections based on network data. Communities for your favorite technologies. Explore all Collectives Ask questions, find answers and collaborate at work with Stack Overflow for Teams.

Ask questions, find answers and collaborate at work with Stack Overflow for Teams. Explore Teams Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. In this example, we use our generalisation of the GraphSAGE algorithm to heterogeneous graphs (which we call HinSAGE) to build a model that predicts user-movie ratings in the MovieLens dataset (see below). The problem is treated as a supervised link attribute inference problem on a user-movie network with nodes of two types (users and movies, both attributed) and links corresponding to user-movie ratings, with integer rating...

To address this problem, we build a model with the following architecture: a two-layer HinSAGE model that takes labeled (user, movie) node pairs corresponding to user-movie ratings, and outputs a pair of node embeddings... These embeddings are then fed into a link regression layer, which applies a binary operator to those node embeddings (e.g., concatenating them) to construct the link embedding. Thus obtained link embeddings are passed through the link regression layer to obtain predicted user-movie ratings. The entire model is trained end-to-end by minimizing the loss function of choice (e.g., root mean square error between predicted and true ratings) using stochastic gradient descent (SGD) updates of the model parameters, with... Specify the minibatch size (number of user-movie links per minibatch) and the number of epochs for training the ML model: (See the “Loading from Pandas” demo for details on how data can be loaded.)

Split the edges into train and test sets for model training/evaluation: The graph data science library (GDS) is a Neo4j plugin which allows one to apply machine learning on graphs within Neo4j via easy to use procedures playing nice with the existing Cypher query language. Things like node classifications, edge predictions, community detection and more can all be performed inside the database and augment the existing graph with learned characteristics. There are many advantages if you follow this path but it might also not always be sufficient: There are highly sophisticated graph machine learning (ML) frameworks which can alleviate these obstacles and once the 'learning' has been performed, the predictions can be returned to Neo4j. This means that the ML part is taken outside Neo4j but, in any case, one seldom performs intensive task on a database which potentially block ingestion and serving downstream tasks (website and alike).

Pytorch Geometric (Pyg) has a whole arsenal of neural network layers and techniques to approach machine learning on graphs (aka graph representation learning, graph machine learning, deep graph learning) and has been used in... Other frameworks (Tensorflow Geometric, StellarGraph, DGL...) can give equivalent results and although Pyg is a popular choice, it all depends on your particular context. Although Pyg has plenty of examples, this repo contains a few ingredients which you will not find elsewhere: More details and context can be found in this article and is a collaboration of Tomaz Bratanic (Neo4j) and Francois Vanderseypen (Orbifold Consulting). Graphs are a powerful data structure used to represent relationships between entities. In many real - world scenarios, these relationships are complex, and the entities themselves can have different types.

This is where heterogeneous graphs come into play. Heterogeneous graphs contain multiple types of nodes and edges, which allows for a more accurate representation of complex systems such as social networks, biological networks, and knowledge graphs. PyTorch Geometric (PyG) is a deep learning library that provides a convenient way to work with graph data in PyTorch. It offers a wide range of tools and functions to handle heterogeneous graphs, making it easier for researchers and practitioners to develop graph - based machine learning models. In this blog post, we will explore the fundamental concepts of heterogeneous graphs in PyTorch Geometric, learn how to use them, and discuss common and best practices. A heterogeneous graph $G=(V, E)$ consists of a set of nodes $V$ and a set of edges $E$.

The nodes and edges can be partitioned into different types. For example, in a social network graph, nodes could represent users, pages, and groups, while edges could represent friendships, likes, and memberships. In PyTorch Geometric, node and edge types are represented as strings. Each node type can have its own set of node features, and each edge type can have its own set of edge features. For example, a node of type “user” might have features such as age, gender, and location, while an edge of type “friendship” might have a feature indicating the duration of the friendship. As shown in the previous example, we can create a heterogeneous graph in PyG using the HeteroData class.

We can add node features and edge indices for different node and edge types. PyG provides several ways to load and preprocess heterogeneous graph data. For example, we can use the DataLoader class to load data in batches. There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. I'm working with Heterogeneous Graphs for Link prediction using different customized datasets. In the current problem/dataset, I want to know if I reached some limitations on the Link pred capabilities or if it's just my lack of knowledge of PyG. I started to work with PyG last year and still consider myself a noob. Any help is welcome. Basically, I want to get 2 disjoint feature-less graphs, create a set of links between them, and check the capability of the GNN to predict these links.

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Link Prediction On Heterogeneous Graphs With Pyg Medium

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