How Graph Neural Networks (GNN) work: Introduction to graph convolutions from scratch

September 5, 2022

Graph neural networks (GNNs) have recently become more and more popular in a variety of fields, including life science, recommender systems, knowledge graphs, and social networks. The breakthrough in the field of graph analysis study is made possible by the strength of GNN in modelling the relationships between nodes in a graph.

Through this blog, we will try to help you in building a basic knowledge of what graph neural networks are, how they work, why we need them, their different types and much more. 

What is Graph?

Graphs are ubiquitous; the definitions of actual world items frequently depend on how they link to other things. A graph is a natural way to represent a collection of things and the relationships among them.

A graph takes the help of nodes and edges to represent this collection and the relationship between them. An object, location, or person can be a node, and the edges indicate how those nodes are related to one another. Based on directional dependencies, the edges might be either directed or undirected.

Figure: (a) Undirected graph & (b) Directed Graph

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What is Graph Neural Network?

Graphs are receiving a lot of attention in the field of machine learning due to their incredibly strong expressive capabilities. Graph Neural Network is an example of a neural network that directly manipulates a graph structure. Node categorization is a common use of GNN.

In essence, each node in the network has a corresponding label, and we want to predict those labels without using ground truth. An embedding is associated with every node. The node's position inside the data space is determined by this embedding. 

The main objective of a GNN architecture is to learn an embedding including neighbourhood information. This embedding might be applied to a number of problems, such as node labelling, node and edge prediction, etc.

In simple words - A subtype of Deep Learning method designed exclusively to do inference on graph-based data is Graph Neural Networks. They are used with graphs and have the ability to carry out prediction tasks at the node, edge, and graph levels.

Why do we need Graph Neural networks?

Recent advances in neural network technology have accelerated the study of pattern recognition and data mining. With end-to-end deep learning models like CNN, RNN, or autoencoders, machine learning tasks have been given new life, such as object detection, machine translation, and speech recognition. Euclidean data's latent patterns can be effectively captured using deep learning (images, text, videos). 

However, Graph Neural Networks (GNN) are helpful when applications, where data is created from non-Euclidean domains and represented as graphs with intricate item interactions and dependencies.

Also, GNNs are needed to solve the challenges related to the Classification of Nodes, Link Forecast, and Classification of Graphs

Types of Graph Neural Network

  1. Recurrent Graph Neural Network

Recurrent Graph Neural Networks (RGNNs) can handle multi-relational graphs where a single node has numerous relations and they can learn the optimal diffusion pattern. Regularisers are used in this form of graph neural network to improve smoothness and reduce over-parameterization. RGNNs produce superior outcomes while utilising less processing power.

Common use cases of RGNNs include:

  1. Text generation,
  2. Speech recognition,
  3. Machine translation,
  4. Picture description,
  5. Video tagging, and 
  6. Text summarizing.

Working of RGNNs: Banach Fixed-Point Theorem is a presumption used in the construction of RGNN. Let (X,d) be an entire metric space and (T: X→X) be a contraction mapping, according to the Banach Fixed-Point Theorem. Once T reaches its one and only fixed point, (x∗), the sequence T n(x) for n→∞ converges to (x). Accordingly, if I apply the mapping T on x k times, x^k should be almost equivalent to x^(k-1).

Figure: Architecture of RGNN

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  1. Spatial Convolutional Network

Spatial graph Convolutional networks learn from graphs that are situated in spatial space by using spatial properties. Similar to the well-known CNN, which dominates the research on image classification and segmentation tasks, the spatial convolution network operates on the same principles. 

Convolution, in essence, is the notion of summing adjacent pixels around a central pixel that are determined by a filter with parametric size and learnable weight. The same concept is applied by spatial convolutional networks, which combine the properties of nearby nodes into the central node.

Spatial graph convolutions due to the confined nature of their filters are often more scalable. The main difficulty is in creating a local invariance for CNNs that operate on core nodes with varied numbers of neighbors.

  1. Spectral Convolutional Network

This kind of graph convolution network has much stronger mathematical underpinnings than other kinds of GNN. Graph signal processing theory serves as the foundation for Spectral Convolutional networks. Graph convolution is also approximated via simplification.

Spectral Convolutional Network is founded on the Graph Signal Processing theory. By using Chebyshev polynomial approximation:

Apart from Chebyshev polynomial approximation, Spectrum Graph Convolutional networks employ the Eigen-decomposition of the graph Laplacian matrix to propagate information along nodes. These networks were motivated by the way waves move across signals and systems.

Applications of GNN

Now that you know what kinds of analyses GNN can carry out, you might be wondering what actual things graphs can do in real life. This section of the article will talk about the practical uses of GNN.

GNN in Computer Vision

Down below are the two effective applications of GNNs in Computer Vision:

  1. The first effective application of using GNNs in Computer Vision is using graphs to describe the relationships between the items identified by a CNN-based detector. Following object detection from the photos, the objects are sent into a GNN inference to predict relationships. A created graph that models the relationships between various items is the result of the GNN inference.

  1. Another application of GNN in computer vision is the creation of images from graph descriptions. Generally, the conventional method of creating images from the text was using GAN or an autoencoder. Graph-to-picture production gives more details on the semantic structures of the images than text-based image descriptions.

Natural Language Processing Using GNN

Natural Language Processing frequently uses GNN. In reality, this is also where GNN starts out. The use of GNN can be advantageous for a variety of NLP tasks, including sentiment classification, text classification, and sequence labelling. 

To anticipate the categories, GNN makes use of the internal relationships between words or texts. For instance, the citation network attempts to predict the label of each publication in the network based on the link between the papers' citations and the terms used in those citations. In addition, rather than using a sequential approach like RNN or LTSM, it may construct a syntactic model by considering many aspects of a phrase.

Use of GNNs in Traffic

A key component of a smart transportation system is the ability to forecast traffic volume, speed, or road density. Utilizing GNNs, we can solve the traffic forecast issue. Imagine the traffic network as a spatial-temporal graph, with nodes representing the sensors placed on roadways, edges representing the separation between pairs of nodes, and dynamic input features representing the average traffic speed within a window for each node.

GNNs in different fields 

There are many other domains apart from NLP and CV where GNN is employed. This includes:

  1. Program verification, 
  2. Programme reasoning, 
  3. Social influence prediction, 
  4. Recommender systems, 
  5. Electrical health records modelling, 
  6. Brain networks, and 
  7. Adversarial attack avoidance.


In this article, we made you familiar with many aspects of GNN. Its understanding and various applications in the real world. GNN is an effective tool for graph data analysis due to its flexibility, expressive power, and simplicity in visualization. It is not just restricted to graphing issues. It is broadly applicable to any topics that may be represented by graphs.

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Comparison between Cloud-Based and On Premises GPUs

Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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October 4, 2022

Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

The table below lists some of these services and compares their cost against their US equivalents. 

According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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