AN OVERVIEW OF GRAPH NEURAL NETWORKS (GNNS), TYPES AND APPLICATIONS

Last updated: June 19, 2025, 22:23 | Written by: Erik Voorhees

Imagine a world where machines understand relationships as deeply as humans do. Graph neural networks and graph convolutional networks are both types of deep learning methods used for analyzing graph-structured data. While they share some similarities, they also have several key differences that make them suited for different tasks. Advantages of GNNs. Graph neural networks (GNNs) offer several advantages compared toWhere algorithms can analyze complex social networks, predict protein interactions, or even recommend the perfect movie based on your friend's viewing habits. Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling graph-structured data. In recent years, GNNs have gained significant attention in various domains. This review paper aims to provide an overview of the state-of-the-art graph neural network techniques and their industrial applications.This is the promise of Graph Neural Networks (GNNs), a revolutionary type of deep learning model designed to operate on graph-structured data.In essence, GNNs are designed to process and learn from data organized as graphs.Graphs are mathematical structures composed of nodes (representing entities) and edges (representing relationships between entities). What Are Graph Neural Networks? Graph Neural Networks (GNNs) are a type of machine learning model specifically designed to work with data that is organized in the form of graphs. A graph consists of nodes which represent individual data points, like people or objects and edges which represent the relationships or connections between those nodes.These powerful models are rapidly gaining traction across various domains, from social sciences to drug discovery, due to their ability to capture intricate dependencies that traditional machine learning algorithms often miss.

This article provides a comprehensive overview of graph neural networks.We will delve into the fundamental motivations behind GNNs, explore their diverse types, and showcase their practical applications across various industries.You'll learn how GNNs leverage message-passing mechanisms to capture complex relationships, and how they differ from traditional neural networks. Graph neural networks (GNNs) are deep learning based methods that operate on graph domain. Due to its convincing performance, GNN has become a widely applied graph analysis method recently. In the following paragraphs, we will illustrate the fundamental motivations of graph neural networks.Get ready to unlock the potential of graph data and discover how GNNs are shaping the future of machine learning.

What are Graph Neural Networks (GNNs)?

Graph Neural Networks (GNNs) are a class of deep learning methods specifically designed to perform inference on data represented as graphs.Unlike traditional neural networks that excel with grid-like data such as images or sequences, GNNs can effectively handle non-Euclidean data structures that represent relationships between objects.Think of a social network where people are nodes and friendships are edges, or a molecule where atoms are nodes and bonds are edges. Graph Neural Network. Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks.GNNs allow us to extract valuable insights and make predictions based on these interconnected relationships.

Essentially, a GNN takes a graph as input, where information is loaded into its nodes and edges. This paper discusses a novel approach to improve the performance and efficiency of machine learning models using frame-averaging techniques.It then processes this information through layers of interconnected nodes, learning representations that capture the graph's structure and properties.This ""graph-in, graph-out"" architecture distinguishes GNNs and allows them to tackle unique problems that are intractable for other deep learning models.

GNNs are particularly useful for tasks that require understanding the relationships between objects. Graph Neural Networks (GNNs) are a recent family of Neural Network models specifically designed to harness the inherent structure and dependencies present in graph-structured data, revolutionizing the way we analyze, model, and make predictions in complex networked structures.They can be used to predict properties of nodes, edges, or even the entire graph. Graph Neural Networks (GNNs) are a type of deep learning model designed to work with data that is best represented as a graph. A graph is made up of nodes (which represent entities, like people in a social network or molecules in chemistry) and edges (which represent the connections or relationships between these entities).For example, a GNN could predict the likelihood of a user clicking on an advertisement in a social network (node-level prediction), predict whether two proteins will interact (edge-level prediction), or classify an entire molecule as drug-like (graph-level prediction).

Why are GNNs Important?

Relational Reasoning: GNNs excel at capturing and reasoning about relationships between entities, a capability crucial for many real-world problems.
Flexibility: GNNs can handle a wide variety of graph types, including directed, undirected, and weighted graphs.
Scalability: Modern GNN architectures can process large and complex graphs efficiently, addressing real-world sized problems with massive datasets.
Non-Euclidean Data: GNNs are specifically designed for non-Euclidean data structures like graphs, which are difficult to handle with traditional neural networks.

The Evolution of Graph Neural Networks

While the concept of GNNs was initially proposed earlier, it's only in recent years that they've gained significant traction. Graph Neural Networks (GNNs) are rapidly advancing progress in ML for complex graph data applications. This primer presents a recipe for learning the fundamentals and staying up-to-date with GNNs. GNNs are advanced neural network architectures designed to process graph-structured data, which are highly effective in various applications such asAdvancements in deep learning, coupled with the increasing availability of graph-structured data, have fueled the rapid development and adoption of GNNs. This document provides an overview of graph neural networks (GNNs). GNNs are a type of neural network that can operate on graph-structured data like molecules or social networks. GNNs learn representations of nodes by propagating information between connected nodes over many layers. They are useful when relationships between objects are important.This evolution has led to the emergence of various GNN architectures, each designed to address specific challenges and optimize performance for different types of graphs and tasks.

The field is dynamic, with researchers constantly exploring new techniques and applications.From the early graph neural network models to the more advanced graph convolutional networks and graph attention networks, the field has witnessed remarkable progress. Graph Neural Networks (GNNs) are a neural network specifically designed to work with data represented as graphs. Unlike traditional neural networks, which operate on grid-like data structures like images (2D grids) or text (sequential), GNNs can model complex, non-Euclidean relationships in data, such as social networks, molecular structures, and knowledge graphs.This continuous evolution ensures that GNNs remain at the forefront of machine learning research, offering innovative solutions for analyzing and modeling complex graph data.

Types of Graph Neural Networks

GNNs come in various forms, each tailored to handle specific types of graph-structured data and learning tasks. 本文翻译自图神经网络综述：《Graph Neural Networks: A Review of Methods and Applications》全文共3.5万字，该论文系统地回顾了图神经网络（GNNs）的方法和应用，包括图卷积网络（GCN）、GraphSAGE、图注意力网络（GAT）等，为图神经网络领域的研究者和实践者提供了一个全面的概述，可以帮助大家更好地理解Here's an overview of some common types of GNNs:

Graph Convolutional Networks (GCNs)

Graph Convolutional Networks (GCNs) are one of the earliest and most widely used GNN variants. Graph neural networks (GNNs) are a type of deep learning mo del that can be used . to learn from graph data. It oﬀers a comprehensive overview of GNN applications, the datasets commonlyThey leverage graph convolutions to aggregate and update node representations based on their local neighborhood.Think of it like smoothing a signal across a graph, where each node's value is influenced by its neighbors.

GCNs operate by propagating information from neighboring nodes through a series of convolutional layers. Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a Graph Neural Network with Pytorch. Training more people? Get your team access to the full DataCamp for business platform. For Business For a bespoke solution book a demo.Each layer aggregates the features of neighboring nodes, weighting them according to the graph's structure. Graph neural networks (GNNs) are a type of deep learning model that can be used to learn from graph data. GNNs use a message-passing mechanism to aggregate information from neighboring nodes, allowing them to capture the complex relationships in graphs.This process allows the network to learn node representations that capture both the node's individual features and its relationship to its neighbors.

For example, in a social network, a GCN could aggregate information from a user's friends to predict their interests or preferences. Graph neural networks, or GNNs, are a type of neural network model designed specifically to process information represented in a graphical format. In traditional neural networks, like convolutional neural networks (CNNs), the data is typically assumed to be in Euclidean space (like text or time data), which can be represented in regular gridThe weights assigned to each friend would reflect the strength of their relationship with the user.

Graph Attention Networks (GATs)

Graph Attention Networks (GATs) introduce the concept of attention mechanisms to the aggregation process. 1- Basics of Graphs. Before jumping into the mechanisms of the Graph Neural Networks, we will start by refreshing some basics on graphs. First of all, graphs are non-euclidean data structures usedUnlike GCNs, which treat all neighbors equally, GATs allow nodes to selectively attend to different neighbors based on their importance. research in this area has been going into great detail. Neural graph networks are being used by practically all researchers in elds such as NLP, computer vision, and healthcare. Graph neural network research evolution Graph neural networks (GNNs) were rst proposed in 2025, but only recently have they begun to gain traction.This allows the network to focus on the most relevant information and ignore irrelevant noise.

GATs use attention weights to determine the contribution of each neighbor to the node's representation. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.These weights are learned during training, allowing the network to adapt to the specific structure and characteristics of the graph.The attention mechanism allows GATs to capture more complex and nuanced relationships between nodes compared to GCNs.

Consider a knowledge graph where entities are connected by different types of relationships.A GAT could learn to attend more strongly to entities that are related to the target entity through a specific type of relationship, such as ""is-a"" or ""part-of.""

Temporal Graph Networks (TGNs)

Temporal Graph Networks (TGNs) are designed to handle graphs that evolve over time. Recently, there is an emergence of employing various advances in deep learning to graph data-based tasks. This article provides a comprehensive survey of graph neural networks (GNNs) in each learning setting: supervised, unsupervised, semi-supervised, and self-supervised learning.These networks can capture the dynamic relationships between nodes and track how the graph's structure changes over time.

TGNs typically incorporate recurrent neural networks (RNNs) or other time-series models to process the temporal information. Recently, graph neural networks (GNNs) have become a hot topic in machine learning community. This paper presents a Scopus-based bibliometric overview of the GNNs research since 2025 when GNN papers were first published.They maintain a memory of past graph states and use this memory to update the node representations over time. In particular, I will focus on four types of GNNs: Graph Convolutional Networks (GCN), Graph Attention Networks (GATs), Temporal Graph Networks (TGNs), and Memory Augmented Graph NeuralThis allows TGNs to capture long-term dependencies and predict future graph states.

Imagine a financial transaction network where nodes represent bank accounts and edges represent transactions.A TGN could track the flow of money between accounts over time and identify suspicious patterns that might indicate fraud.

Memory Augmented Graph Neural Networks

Memory Augmented Graph Neural Networks enhance GNNs by incorporating external memory modules. We re going to build GNNs using the message passing neural network framework proposed by Gilmer et al. using the Graph Nets architecture schematics introduced by Battaglia et al. GNNs adopt a graph-in, graph-out architecture meaning that these model types accept a graph as input, with information loaded into its nodes, edges andThese modules allow the network to store and retrieve information about the graph, enabling it to reason about long-range dependencies and complex relationships. Flexibility: GNNs can work with a variety of different graph types, which include the directed, undirected, and weighted graphs. Scalability: Modern GNN architectures can process large, highly complex graphs efficiently and hence address real-world-sized problems with massive datasets.The memory module effectively acts as a knowledge base that the GNN can consult during its computations.

By augmenting the GNN with external memory, it can learn to store important information about the graph and retrieve it when needed.This can be particularly useful for tasks that require reasoning about the global structure of the graph or understanding the context of specific nodes and edges.

For example, in a recommender system based on a social network, a memory-augmented GNN could store information about users' past interactions and preferences in the memory module. Graph neural networks are a type of neural network that is designed to process graph-structured data. Graphs are mathematical constructs that are used to represent objects and theirThis information could then be used to provide more personalized recommendations.

Advantages of Using GNNs

Graph Neural Networks offer several advantages over traditional machine learning methods when dealing with graph-structured data:

Capturing Relationships: GNNs are designed to explicitly model relationships between entities, making them ideal for tasks where these relationships are crucial.
Handling Non-Euclidean Data: GNNs can effectively process non-Euclidean data structures like graphs, which are difficult to handle with traditional neural networks.
Flexibility: GNNs can work with a variety of different graph types, including directed, undirected, and weighted graphs.
Scalability: Modern GNN architectures can process large and complex graphs efficiently, enabling them to address real-world problems with massive datasets.
Node, Edge, and Graph Level Predictions: GNNs can be used for node-level, edge-level, and graph-level prediction tasks, providing flexibility for a wide range of applications.

Applications of Graph Neural Networks

GNNs are finding applications in a wide range of domains, leveraging their ability to analyze and model graph-structured data. GNNs come in various forms, each tailored to handle specific types of graph-structured data. Some common types of GNNs include: Graph Convolutional Networks (GCNs): GCNs are one of the earliest and most widely used GNN variants. They leverage graph convolutions to aggregate and update node representations based on their local neighborhoodHere are some prominent examples:

Social Network Analysis

GNNs are well-suited for analyzing social networks, where users are nodes and relationships are edges. Graph neural networks help to process and analyze complex graph-structured data, unlocking new possibilities across a wide range of applications.They can be used for tasks such as:

Community Detection: Identifying groups of users with similar interests or connections.
Link Prediction: Predicting new relationships between users based on their existing connections.
Recommendation Systems: Recommending content or users based on social connections and preferences.

Drug Discovery

GNNs are being used in drug discovery to:

Predict Molecular Properties: Predicting the chemical and physical properties of molecules based on their structure.
Drug-Target Interaction Prediction: Identifying potential drug candidates that can interact with specific target proteins.
Drug Repurposing: Identifying existing drugs that can be used to treat new diseases.

Recommender Systems

GNNs can be used to build more effective recommender systems by:

Modeling User-Item Interactions: Representing users and items as nodes in a graph and modeling their interactions as edges.
Capturing Collaborative Filtering Effects: Leveraging the relationships between users and items to make personalized recommendations.
Incorporating Side Information: Integrating additional information about users and items, such as demographics or product attributes, into the graph.

Natural Language Processing (NLP)

GNNs are finding applications in NLP for tasks such as:

Sentiment Analysis: Analyzing the sentiment expressed in text by modeling the relationships between words and phrases.
Machine Translation: Improving the accuracy of machine translation by capturing the dependencies between words in different languages.
Question Answering: Answering questions based on knowledge graphs that represent relationships between entities.

Computer Vision

GNNs are being used in computer vision for tasks such as:

Image Classification: Classifying images by modeling the relationships between objects in the scene.
Object Detection: Detecting objects in images by modeling the relationships between different regions of the image.
Scene Graph Generation: Generating scene graphs that represent the objects and their relationships in an image.

Building a Graph Neural Network

While building a GNN from scratch can be complex, various frameworks and libraries simplify the process.PyTorch and TensorFlow, along with specialized libraries like PyTorch Geometric and DGL (Deep Graph Library), provide the necessary tools and abstractions to define and train GNN models.

The general process involves:

Data Preparation: Representing your data as a graph, defining nodes, edges, and their associated features.
Model Definition: Choosing a GNN architecture (GCN, GAT, etc.) and defining the layers and parameters.
Training: Training the model using a labeled dataset and an optimization algorithm.
Evaluation: Evaluating the model's performance on a held-out test set.

The message passing neural network framework is a popular approach for building GNNs.This framework involves defining how information is aggregated from neighboring nodes (message passing) and how node representations are updated based on these messages.

Challenges and Future Directions

Despite their potential, GNNs still face several challenges:

Scalability to Extremely Large Graphs: Processing massive graphs with billions of nodes and edges can be computationally expensive.
Over-smoothing: In deep GNNs, node representations can become too similar, hindering performance.
Dynamic Graphs: Handling graphs that evolve over time poses challenges for existing GNN architectures.
Interpretability: Understanding why a GNN makes a particular prediction can be difficult.

Future research directions include:

Developing more scalable GNN architectures.
Addressing the over-smoothing problem.
Designing GNNs for dynamic graphs.
Improving the interpretability of GNNs.
Exploring new applications of GNNs in various domains.

Frequently Asked Questions (FAQs)

What is the difference between a GNN and a CNN?

Traditional neural networks like Convolutional Neural Networks (CNNs) are designed for grid-like data, such as images.They assume a fixed spatial relationship between neighboring elements.GNNs, on the other hand, are specifically designed for graph-structured data, where the relationships between nodes are irregular and non-Euclidean.GNNs use message passing to aggregate information from neighboring nodes, allowing them to capture the complex relationships in graphs.

Are Graph Convolutional Networks (GCNs) and Graph Neural Networks (GNNs) the same?

No.GCNs are a *type* of GNN.Think of GNN as the broader category.GCNs use convolutional operations tailored to the graph structure to learn node embeddings.Other types of GNNs exist, such as GATs (Graph Attention Networks) and TGNs (Temporal Graph Networks), each with its own specific approach to processing graph data.

What type of data is appropriate for a GNN?

GNNs are appropriate for any data that can be represented as a graph.This includes social networks, knowledge graphs, molecular structures, citation networks, and many other types of data where relationships between entities are important.

How can I get started with GNNs?

Start by learning the basics of graph theory and deep learning.Then, explore the various GNN architectures and libraries available, such as PyTorch Geometric and DGL.Experiment with different datasets and tasks to gain practical experience.Many online resources, tutorials, and courses can help you get started with GNNs.

Conclusion

Graph Neural Networks have emerged as a powerful tool for analyzing and modeling graph-structured data.Their ability to capture complex relationships and handle non-Euclidean data makes them ideal for a wide range of applications, from social network analysis to drug discovery.By understanding the different types of GNNs and their advantages, you can unlock the potential of graph data and solve real-world problems.The field is constantly evolving, with new architectures and applications emerging regularly. Graph neural networks (GNNs) are at the forefront of machine learning innovation.Whether you're a seasoned data scientist or just starting your journey, exploring GNNs can open up exciting new possibilities.

Ready to dive deeper?Explore the resources mentioned in this article, experiment with different GNN architectures, and contribute to the growing community of GNN researchers and practitioners.The future of machine learning is connected, and GNNs are leading the way.Consider using your knowledge to make predictions based on relationships in your area of expertise.