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Search Icon Icon to open search CS224W - Lecture 1.1 - Why Graphs Last updated
Aug 31, 2022

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https://www.youtube.com/watch?v=JAB_plj2rbA

# Why Graphs?Graphs are a general language for describing and analyzing entities with relations/interactions. Many types of data can be represented as graphs:Event Graphs Computer Networks Disease Pathways Food Webs Particle Networks Underground Networks Social Networks Economic Networks Communication Networks Citation Networks Internet Network of Neurons Knowledge Graphs Regulatory Networks Scene Graphs Code Graphs Molecules 3D Shapes. # Types of Networks and GraphsNetworks or Natural GraphsUnderlying domains can naturally be represented as graphs. Social Networks Communication and transactions Biomedicine Brain Connections GraphsInformation/Knowledge Software Similarity networks Relational structures Sometimes, the distinction between networks and graphs is blurred. [?] How do we take advantage of relational structure for better prediction?Complex domains have rich relational structure, which can be represented as a relational graph. By explicitly modeling relationships, we achieve better performance. Modern deep learning toolbox is designed for simple sequences and grids.Such as images, audio, video, etc. Graph/Networks are much harder to process because they are more complex.They have arbitrary size and complex topological structure (i.e., no spatial locality like grids). No fixed node ordering or reference point. Often dynamic and have multimodal features. Graphs are the new frontier of deep learning. # Representational LearningRepresentational learning allows us to automatically learn the features. We don’t have to perform manual feature engineering. To perform Representational Learning, we can map nodes to $d$-dimensional embeddings such that similar nodes in the network are embedded close together . # Course OutlineTraditional methods Methods for node embeddings Graph Neural Networks Knowledge graphs and reasoning Deep generative models for graphs Applications to Biomedicine, Science, Industry