# 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

picture source: researchgate.net

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

picture source: researchgate.net

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

Conclusion

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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October 18, 2022

### A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

• Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

• MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
• CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
• CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
• Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

• Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

October 18, 2022

### Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

## The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

• Highly calibrated cameras that take a photograph of the image from various angles.
• Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

• Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

• Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

• Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

• Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

• Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

• 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
• It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
• They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
• It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
• It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

October 18, 2022

### A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

### GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

• The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
• Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
• GAUDI also removes GANs pathologies like mode collapse and improved GAN.
• GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

• Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
• This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
• The scenes are thus synthesized by interpolation within the hidden space.
• The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
• A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.