If you’re eager to explore sharp boundaries in the realm of deep learning with graphs and hypergraphs, look no further! DeepHypergraph (DHG) is your go-to deep learning library built on the renowned PyTorch platform. This article will provide a user-friendly guide on how to install, visualize, and learn using your new favorite library – DHG.

Installation

Getting started with DHG is straightforward. The recommended stable version at the moment is 0.9.4. You can install it using pip as follows:

pip install dhg

If you want to experiment with the nightly version (0.9.5), which includes the latest features, you can run:

pip install git+https://github.com/iMoonLab/DeepHypergraph.git

Quick Start

Let’s dive into visualization first! You can create graphs, hypergraphs, directed graphs, and bipartite graphs with ease using DHG’s visualization tools.

import matplotlib.pyplot as plt
import dhg

# Draw a graph
g = dhg.random.graph_Gnm(10, 12)
g.draw()

# Draw a hypergraph
hg = dhg.random.hypergraph_Gnm(10, 8)
hg.draw()

# Show figures
plt.show()

Learning on Structures

DHG allows you to smoothly process features using Graph Neural Networks (GNNs) or Hypergraph Neural Networks (HGNNs). Think of your data as a complex web: each connection (edge) influences the node (vertex) points, passing information along them either as simple messages from one vertex to another or more complex exchanges with hyperedges.

Example Code for Learning

Here’s how you can achieve this:

import torch
g = dhg.random.graph_Gnm(5, 8)
X = torch.rand(5, 2)
X_ = g.smoothing_with_GCN(X)

This code snippet uses a GCN Laplacian matrix to smoothen the features represented by X, giving you refined, connected data points just like adjusting the focus of a camera to get a clearer picture.

Available Datasets

DHG provides a variety of datasets for both vertex classification and user-item recommendations, including:

• Cora: A citation network dataset.
• PubMed: Another citation network dataset.
• MovieLens1M: A dataset tailored for movie recommendations.

Troubleshooting

While getting started with DHG, you might encounter a few bumps along the way. Here’s what to keep in mind:

• If you receive errors regarding dependencies, ensure that all required packages are installed. Use pip install -r requirements.txt in the project directory.
• If you’re using the nightly version and face instability, consider reverting to the stable version using the first installation command suggested.

For further assistance with troubleshooting or to stay updated, you can always check the Discussions section on GitHub.

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