Why do we need Graph Neural Networks?
Indeed, traditional deep learning models, like Convolutional Neural Networks and Recurrent Neural Networks, are well adapted to data organized in grids, such as images, or sequences, such as text. They are not designed to process graphs, since intrinsic relationships between nodes are not considered.
This is why GNNs are so critical for graph data.
- Non-Euclidean Structure: For images, a spatial relationship among the pixels is fixed in advance, but graphs are of non-Euclidean nature. In other words, the ordering of the nodes of the graph does not matter—furthermore, it can only be established from its neighboring nodes whether a node of interest is considered important or not.
- Variable Node Size: In a graph, nodes can have varying size of information. On the other hand, CNNs and RNNs can only suppose each data point to be of fixed size.
- Long-Range Dependencies: The relations in a graph can actually span long distances, something that methods developed on local filters within traditional CNNs would particularly struggle to capture.
GNNs overcome some of these challenges by embedding the graph structure into the learning framework: the structure is then used to exploit any inter- or intra-relationships that might exist between different nodes so as to learn the right informative representations programmed with each node and, in the end, gain insight into the overall graph.
Graph Neural Networks: An In-Depth Introduction and Practical Applications
Graph Neural Networks (GNNs) are a class of artificial neural networks designed to process data that can be represented as graphs. Unlike traditional neural networks that operate on Euclidean data (like images or text), GNNs are tailored to handle non-Euclidean data structures, making them highly versatile for various applications. This article provides an introduction to GNNs, their architecture, and practical examples of their use.
Table of Content
- What is a Graph?
- Key Concepts in Graph Neural Networks
- Why do we need Graph Neural Networks?
- How do Graph Neural Networks Work?
- Popular Graph Neural Networks Models
- Training Graph Neural Networks : Implementation
- Benefits and Limitations of GNNs
- Real-World Applications of Graph Neural Networks
- Future Aspects of GNNs