Graph Convolutional Networks in Action

Let’s have a look at the following image.

What do you see there? A beautiful scenic sunset, right? That’s how we can categorize the image in simple words. Let’s think about it from a Deep Learning perspective, I’m expecting a classical image labeling paradigm. Most algorithms label this image as a sunset. But, observe it closely. A lot more labels of significant importance are still there, right? There is a soothing lake, a small boat anchored in the water, trees covered by the other side, clouds of course …

This is the fundamental building block of most recent researches based on graphs. People across the research communities are trying to incorporate the prior knowledge to get better results.

Believe me, a single post won’t be enough to cover this topic. But wouldn’t be awesome if we had one, right? Don’t worry guys, I’ve one ongoing series on Graphs and advanced algorithms named Graph Theory : Go Hero. I’ve tried to cover every single aspect of graphs, terminologies and algorithms. It’s not yet completely done, but it’s progressing. Checkout the above link to have a quick refresher on graphs.

In nutshell, a graph is a data structure which has nodes to keep information and edges to show relationship among them.

The circles depicted in different colors are the nodes and the lines connecting two nodes are the edges and they have weights associated with it. In a more realistic way, nodes could be conceptualized as different cities and edges as the roads connecting them. The weights assigned to…

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