Python graph neural networks

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okay, let's dive deep into graph neural networks (gnns) with python. this tutorial will cover the fundamentals, common architectures, implementation using popular libraries, and a practical example.

**part 1: introduction to graph neural networks (gnns)**

* **what are graphs?**

a graph is a data structure consisting of *nodes* (or vertices) and *edges* that connect these nodes. graphs are used to represent relationships between entities.

* nodes: represent objects, entities, or individuals. examples: people in a social network, molecules in a chemical compound, cities on a map.
* edges: represent the relationships or interactions between nodes. examples: friendships, chemical bonds, roads between cities.

graphs can be:
* *directed* (edges have a direction, e.g., "follows" on twitter) or *undirected* (edges have no direction, e.g., a friendship).
* *weighted* (edges have weights representing the strength or cost of the relationship) or *unweighted*.
* *cyclic* (contain cycles) or *acyclic* (no cycles).

* **why gnns?**

traditional neural networks are designed for data with a grid-like structure (e.g., images) or sequential structure (e.g., text). gnns are specifically designed to process data represented as graphs. they allow us to:

* learn representations of nodes, edges, or entire graphs.
* perform node classification (predicting the category of a node).
* perform link prediction (predicting whether a link exists between two nodes).
* perform graph classification (predicting the category of an entire graph).
* solve problems in various domains: social networks, molecular biology, recommendation systems, knowledge graphs, etc.

* **the core idea: message passing**

the fundamental concept behind gnns is *message passing* (also known as neighborhood aggregation). nodes exchange information with their neighbors iteratively. each node aggregates information from its neighbors ...

#Python #GraphNeuralNetworks #numpy
Python
graph neural networks
GNN
deep learning
machine learning
graph representation
node embeddings
edge features
graph data
semi-supervised learning
PyTorch Geometric
TensorFlow GNN
message passing
community detection
graph classification

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