Convolutional Network is a type of Neural Network. Neural Networks are a special kind of deep learning model. Typically, machine learning or deep learning comprises rigorous and expensive algorithms due to its complicated task. Similarly, deep learning models on graphs are even more complicated. Graph Convolutional Networks are primarily used in the purpose of image classification. Learn more about convolutional neural network.
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Through the last decade, the application of data science has increased enormously. In this data-rich world, the learning model approach brought great results and accurate predictions. Graphs are useful for many information systems.
From biological protein interactions to internet connectivity and WorldWideWeb, graphs represent all these systems. Also, implementing neural networks through graphical structure lets the computer understand the properties of an image. This model is one of the most advanced real-world applications of the graph. Let us discuss these algorithms in detail:
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How Neural Networks are Built
Neural Networks are one of the most advanced techniques of data science and deep learning. Neural Networks are useful in many applications, from Stock Market prediction to image classification, speech or character recognition and even in sequence analysis.
The first concept of the Neural Network came from biological perspectives. Scientists conducted experiments in which nerves for vision are connected with the hearing centres of the brain. Eventually, the organism learnt to see through the hearing centre of the brain too. Even further experiments proved that every centre of the brain could perform every action.
Approaches started to begin to mimic the human brain informing computer algorithms. So, similarly, computer scientists also thought that there should be a single algorithm which is capable of solving all computer brain learning problems. That is how the neural network came to birth.
A neural network consists of multiple layers of neurons. Each neuron is typically a graph node. Each neuron of each layer is connected with all the neurons of the next layer through a weighted edge—the weights of the edge act as a coefficient of the layer value calculation.
Through backpropagation, coefficients change to fit the model with sample training examples. Ultimately, a single neuron from the last layer gives the output. In the following image, the structure of a neural network is explained.
Read: Using Convolutional Neural Network For Image Classification
Graph Convolutional Networks
Convolutional Networks are 3-dimensional neural networks. Most practical uses of Convolutional Neural Networks include image classification and recognition, natural language processing and speech recognition. These models are usually more complex than the usual 2-dimensional neural network models.
In this architecture, layers of different neurons are assembled. The parameter of dimensions is variable in different layers to make the model recognise parameters. For instance, images are two dimensional, and, in the meantime, the colour of each point also play a crucial role. Hence, three different parameters emerge. To deal with such complexities, Conv Nets play a significant role.
These many 3d matrices of different dimensions work at multiple levels of the neural network. Eventually, the ‘z’ dimension fit the output parameter of the network. The conveyance of information from one level to another can take place through a variety of different algorithms.
For instance, FC (Fully Connected), Pooling and ReLU are some crucial algorithms regarding this.
Generally, the node values of the neural network are denoted with, where ‘l’ signifies the layer number. So, a0 is the input matrix.
On the other hand, the last layer node defines the output. Say, there are ‘L’ layers. Therefore aL denotes the output of the neural network.
The above image depicts a convolutional neural network in the implementation of image classification. The parameters are set for a dog, cat, bat and bird.
The node value of a particular internal layer is calculated through previous layer values.
Here, is the adjacency matrix and, f is the defining function. Every graph convolutional network layer can be written using this expression. In this way, a graph convolutional neural network typically works.
Applications of Graph Convolutional Networks
- Graph Convolutional Networks generate predictions over physical systems, such as graphs, their interactive approach and applications. GCN also provides accurate information about the properties of real-world entities and physical systems (dynamics of the collision, objects trajectories).
- GCNs are used to perform image differentiation problems. The model it follows is known as ‘Zero-Shot Learning’. The main motive of this model is to identify an unknown labelled image and group it into known ones. They also gather semantic information of these labels and categorise them.
- GCNs can take a certain length of molecular fingerprints as input and generate predicted molecular structures. MolGAN is one kind of Graph Convolutional network which helps to create new molecular structures with various features in it. In this way, it allows scientists to invent modern molecular structures day by day.
- GCN is applicable for solving various problems related to research operations and combinatorial optimisation applications. Graph Convolutional Networks play a pivotal role in solving salesman problems, quadratic assignment problems, and many more. With the help of the input graph, it can outclass traditional complex algorithms.
Karate Club of Zachary
Another significant application of Graph Convolutional Networks is to solve community prediction problems, such as Karate Club of Zachary. This problem is based on the dispute between the administrator and the instructor of the club.
We have to figure out which side every member of the karate club would select. This problem gets resolved by using semi-supervised learning techniques. By using just two labelled nodes, Tobias Jepsen was able to fix the problem and reach near-perfect accuracy in terms of predicting those two communities.
Also Read: Neural Network Project Ideas
Now let’s take a look at the following images and, you would be able to get some insights about the Karate club problem and its proper calculations using Graph Convolutional Networks.
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By reading this article, you would be able to understand what Graph Convolutional Networks are, how Neural Networks are built, a brief idea of GCN and how it works, and various crucial aspects and applications of GCN including Zachary Karate Club problem.
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What are the limitations of using neural networks?
The most significant drawback of employing neural networks to solve a problem is that the outcome is not properly explained, which might be difficult for many users. When compared to other machine learning techniques, neural networks require a lot more data to function well. They cost more to compute than any other traditional machine learning algorithm. From the ground up, training highly deep neural networks can take many weeks.
Which CNN model is considered to be the most optimum for image classification?
For image classification, the use of VGG-16, which stands for Very Deep Convolutional Networks for Large-Scale Image Recognition, is preferred. Outside of ImageNet, VGG, which was built as a deep CNN, outperforms baselines on a broad range of tasks and datasets. The model's unique characteristic is that rather than focusing on adding a huge number of hyperparameters, more emphasis was made on including superior convolution layers as it was being developed. It contains a total of 16 layers, 5 blocks, and a maximum pooling layer for each block, making it a huge network.
Why is it hard to perform CNNs on graphs?
It's difficult to execute CNNs on graphs because of their arbitrary size. Furthermore, there is no spatial locality in the graph due to its complicated topology, which is another reason why CNNs aren't employed in graphs. On the graph, GCNs are used for semi-supervised learning. The GCN's fundamental principle is to take a weighted average of all the node attributes of all its neighbors (including itself), with lower-degree nodes receiving higher weights. The generated feature vectors are then fed into a neural network for training.