Graph Neural Networks and their applications. This report summarizes the need for Graph Neural Networks and analyzes one particular architecture – the Gated Graph Convolutional Network.

Over the past three years, Graph Neural Networks(GNN) has grown exponentially in popularity. This is due to their ability to learn from graph data efficiently. There is an intricate and cohesive network connection behind most systems that defines the interaction between the network components. This interaction can be represented as a graph. To model these systems, it is essential to understand the network behind them. Graph Neural Networks help us take advantage of the network's relational structure to model it better and predict the outcome.

A graph is a structured non-linear datatype with nodes(also called vertices) and edges. It is mathematically represented as G(V, E). For example, a molecule of NO₂ can be considered a graph where the nitrogen atom and two oxygen atoms are considered the node, and the bonds between the atoms are considered to be edges. Another example of a graph can be your family, where each person is a node, and the relationship between two people is the edge.

NO₂ as a graph (Image by Author)

A graph can have labels on both its nodes and edges. It can either be numerical or textual. Each node will have some features that define the node. In a graph of NO₂, elements like the atomic number, atomic weight, and the number of valency electrons of each node can be its respective features. The edges might or might not have features depending on the type of graph. In NO₂, the edges' features can be the bond strength, bond type(single bond or double bond), etc.

Graphs are classified on many different bases. The most common one is based on the edges of the graph. These are directed and undirected graphs. In a directed graph, the edges from one node to another are directional, while in an undirected graph, the nodes are connected through edges, and no direction is present.

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Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different

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Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.