A Guide To Graph Neural Network

We bet you remember Helen Parr (AKA Elastigirl) – later known as Mrs. Incredibles, do you? Mr. and Mrs. Incredibles often worked together, sharing a strong bond as superheroes. That’s pretty much how GNN (Graph Neural Network) works - it recognizes the connections between data points (superpowers) and exchanges information to make the bigger picture clear and solve issues (defeat villains).

Here’s another example: you know how social media platforms function – people share information about themselves, like their interests, hobbies, career updates, etc., with other friends. These connections can then be represented as a graph, where each point is a node and the connections between them show the relation between the nodes.

The goal of a Graph Neural Network is to help these people (nodes) share and update their information with their friends (other nodes) meaningfully to make predictions and understand the patterns and relationships between the nodes in the network.

Read on as we explore Graph Neural Networks as well as its history, benefits, working and future trends!
 

What Is A Graph Neural Network?

Let's understand what “graphs” are before we get into the Graph Neural Network. If we put the definition in simple terms, a graph is a way to show the connections between different things – a map of relationships. It consists of points (nodes) and lines (edges) connecting these points, where nodes represent objects or data points and edges show how they are related.

Now, if we talk about a Graph Neural Network, it is a way to help us visually represent graphs and understand and analyze them better – much like a detective for graphical data! Since graphs have powerful expressive capabilities, they are getting significant interest in machine learning (ML) and deep learning.

Businesses have been using GNNs to enhance drug discovery, fraud detection and recommendation systems by identifying patterns in data relationships. Yes, graphs can be fun and have many advantages in industrial applications. Recent research even applied GNNs to transportation maps to improve arrival/ departure time predictions and help unlock insights from data stored in graph databases.

From discovering the graphs to Graph Neural Networks, we have come a long way. Get your super suit ready because we are going back in time!
   

The History Of Graph Neural Network

Graph theory has a long history, dating back to the 18th century. Early algorithms for analyzing and manipulating graphs, like Dijkstra's algorithm (1956) for shortest path and the PageRank algorithm (1996) for web page ranking, laid the foundation for modern graph-based data analysis.

However, before that, in 1944, Warren McCullough and Walter Pitts, early researchers at the University of Chicago, introduced the idea of neural networks. They later became founding members of the first cognitive science department at MIT after moving there in 1952.

The concept then enjoyed a resurgence in the 1980s and soon fell into eclipse in the first decade of the new century. However, the concept of applying neural networks to graph-structured data gained significant attention in the late 2010s due to the power of graphics chips.

One of the early influential papers in this field was "Semi-Supervised Classification with Graph Convolutional Networks" by Thomas Kipf and Max Welling, published in 2016 and revised later in 2017. This paper introduced a Graph Convolutional Network (GCN) architecture, considered the foundation of the GNN model.

While GNNs have shown great promise, there are still challenges. The ongoing research aims to address the challenges and the field of Graph Neural Networks will continue to evolve.

Well, you might be wondering, how does GNN work? Let’s explore!
 

How Do Graph Neural Networks Work?

Graph Neural Networks (GNNs) are essentially machine learning systems that take graphs as input and make decisions based on the data. This approach is rooted in graph theory, which suggests that systems and networks can be represented as graphs, using nodes and edges.

Online social networks are just one of many real-world applications linked to graph theory. Since social networking is a relatable analogy, we'll use it as an example. Let’s imagine three people on a social media platform: Person A, Person B and Person C.
 

• Directed Graphs

  If Person A searches for B or C’s profile on the platform but Person B or C does not search for Person A or each other, it can be presented as directed graphs. It reflects a one-way relationship that a node has with one or more.

• Undirected Graphs

  When Person A and B search for each other’s profile and Person A and C also search for each other’s profile, it reflects a two-way relationship. The point to note here is when Person A and B are connected and A and C are also connected, C may have information about B, A being the mutual to both B and C.

 
Based on directed and undirected graphs, various types of Graph Neural Networks have been classified according to their nature and applications. Let’s have a look at them!
 

Types Of Graph Neural Networks

The Graph Neural Network can be classified into three types as follows.
 

• Recurrent GNN

  A Recurrent GNN operates with cyclic connections among nodes, dynamically enabling information exchange between them within the same cycle. It's based on a mathematical concept called the Banach Fixed-Point Theorem.

• Spatial GNN

  Spatial GNN resembles Convolutional Neural Networks (CNNs) in their approach. They aggregate information from neighboring nodes and convey it to a central node.

• Spectral GNN

  Spectral GNN utilizes mathematical matrix theory. Unlike other GNNs that rely on nodes and topology, Spectral GNN leverages concepts of eigenvalues and eigenvectors to derive outcomes from the given graphical data.

Too complex to understand?
 
We bet understanding the applications makes it easy – so let's head on to the next section.
 

What Are The Applications Of Graph Neural Network

Graphs and neural networks have always been part of theoretical data analytics. However, as discussed before, we have now found a way to apply them in real-life scenarios and develop applications based on them. Here are the top use cases of GNNs:
 

• Fraud Prevention

  GNN analysis can identify new accounts linked to past malicious behavior, flagging them as potentially problematic. This predictive approach, known as link analysis, helps anticipate future actions by malicious entities.

• Online Safety

  GNNs are crucial for improving online safety in social media. Using graph theory, GNNs help uncover important patterns in user activity, forming the basis of social media analysis for cybersecurity.

• Medicine

  GNNs analyze patient data, including age and gender, revealing trends tied to these factors. It aids medical professionals in recognizing demographic-specific diagnoses and treatment patterns.

• Computer Vision

  Computer vision, an AI (Artificial Intelligence) system, scans and categorizes images, helping us summarize insights and generate predictions. For instance, GNNs enhance pattern recognition, a crucial step for cancer screenings through X-ray analysis.

• Geolocation

  GNNs enhance geolocation software like Google Maps, providing precise predictions of ETA (Estimated Arrival Time). They analyze various factors, such as traffic and weather, to compute accurate journey times for drivers.

Well, mathematics turns out to be quite useful in GNN’s applications. Remember when we mentioned every superhero has unique talents? Well, let’s see what unique talents Graph Neural Networks have! (Yes, we are still into The Incredibles analogy.) #WeWantIncrediblesThree
 

Advantages Of Graph Neural Network

There can be various advantages of Graph Neural Network based on its applications. However, here are the key advantages of Graph Neural Network.
 

• Customizability

  GNNs' customizability allows researchers to craft models tailored to unique problems, promoting the creation of tailored solutions for specific challenges and domains.

• Compatibility

  GNNs can be seamlessly integrated with diverse machine learning methods like deep learning. This fusion creates hybrid models, capitalizing on the strengths of each, leading to improved performance in many tasks.

• Robustness

  GNNS are less affected by disruptions in graphs, ensuring consistent performance across different situations, enhancing reliability.

   

Future Trends In Graph Neural Network

There is no doubt that GNNs are becoming a big deal when it comes to learning different graph structures. Moreover, the fact that any improvement in this area cultivates advancements across a wide range of domains shows the promising future of Graph Neural Networks.
 

• AI Research

  A recent study shows the popularity of Graph Neural Networks, as it analyzed the exponential growth of published research, with a striking +447% annual increase during 2017-2019. GNNs are witnessing impressive growth in AI research across various domains, which will impact various associated fields.

• Recommendation Systems

  GNNs hold great promise for enhancing recommendation systems as well. By modeling the complex relationships among users, items and interactions as graphs, GNNs will provide more accurate and personalized recommendations. This will improve user experience and increase the success of recommender systems in the future.

• Traffic Prediction System

  Another trend to look out for is traffic prediction algorithms. GNNs will be applied to transportation maps to improve the accuracy of estimated arrival time (ETA). The idea is to use GNNs to represent the transportation network, capturing the structure of the network and its dynamics.

Should we roll the end credits already?
 

End Credits

In conclusion, Graph Neural Networks (GNNs) are the strategic leaders in the superhero world of data analysis, just like Mr. Incredibles. GNNs harness the collective strength derived from information shared between nodes and offer valuable insights that would be impossible to obtain by looking at individual data points in isolation.

If you want to maximize your data's potential and confidently make decisions, Graph Neural Networks is what you need!