Graph neural networks — their need, real-world applications, and basic architecture with the NetworkX library
In this post, we are going to investigate a relatively newer field in deep learning which involves graphs — a very important and widely used data structure. This post encompasses the basics of graphs, the amalgamation of graphs and deep learning, and a basic idea about graph neural networks and their applications. We will also briefly discuss on how to build graphs with a Python library called NetworkX
So, let’s dive right in!
What are graphs?
In the world of computer science, graphs are a type of data structure having two components: Nodes (or vertices) and edges, which connect two nodes. Thus, a graph can be defined as a collection of loosely inter-connected nodes via edges.
Thenodes of a graph can be homogenous with all nodes having a similar structure, or heterogenous nodes having different types of structure. The edges define the relationship one node has with another. Edges can be bidirectional (from one node u to another v and vice versa), or unidirectional (from one node u to another node v). Edges can also be weighted — having a weight assigned to the edge that might depict the edge’s cost or importance.
An example: Let us suppose a graph to be considered as a network of cities — the cities under observation being nodes and the roads connecting them being edges. Now, there can be various types of relevant problems that can be solved with graphs, such as finding out the shortest distance between cities (where roads can also be weighted as per the condition of the roads or traffic), or finding the cities which are well-connected to each other, etc.
What are Graph Neural Networks (GNN)?
Graphs have tremendous expressive powers and are therefore gaining a lot of attention in the field of machine learning. Every node has an embedding associated with it that defines the node in the data space. Graph neural networks refer to the neural network architectures that operate on a graph. The aim of a GNN is for each node in the graph to learn an embedding containing information about its neighborhood (nodes directly connected to the target node via edges). This embedding can then be used for different problems like node labelling, node prediction, edge prediction, etc.
Each node and its neighborhood
Thus, after having embeddings associated with each node, we can convert edges by adding feed forward neural network layers and combine graphs and neural networks.
Need for Graph Neural Networks
The need for graph neural networks arose from the fact that a lot of data available to us is in an unstructured format. Unstructured data is data that has not been processed or does not have a pre-defined format which makes it difficult to analyze. Examples of such data are audio, emails, and social media postings. To make sense of this data and to derive inferences from it, we need a structure that defines a relationship between these unstructured data points. The existing machine learning architectures and algorithms do not seem to perform well with these kinds of data. The primary advantages of graph neural networks are:
- The graph data structure has proven tremendously successful in the field of computer science while working with unstructured data.
- Graphs are helpful in defining concepts which are abstract, like relationships between entities. Since each node in the graph is defined by its connections and neighbors, graph neural networks can capture the relationships between nodes in an efficient manner.
Thus, developing GNNs for handling data like social network data, which is highly unstructured, is an exciting amalgamation of graphs and machine learning which holds a lot of potential.
Real-Life Applications of Graph Neural Network
Being introduced recently in 2018, the GNNs still have a lot of real-life applications because their architecture resonates with the irregularity in data collected from various sources. Currently, GNNs have been the hot topic for:
Social Network Analysis — Similar posts prediction, tags prediction, and recommending content to users.
Natural Sciences — GNNs have also gained popularity in dealing with molecular interactions like protein-protein interactions.
Recommender Systems — A heterogenous graph can be used to capture relationships between users and items to recommend relevant items to a buyer.
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