Graph Signal Processing Lecture
Network theory has very many application areas, where graphs are of primary importance, in e.g.,:
-Communication networks
-Epidemiology
-Systems Biology
-Social networks.
Social Media (e.g., Twitter, Facebook, Instagram, to name a few) has had a tremendous growth in the past 20 years. Social Media Analysis has very many applications, e.g.,:
-Recommendation Systems
-Sentiment Analysis
-Information Diffusion
-Web Search.
This lecture overviews Graph Signal Processing that has many applications in Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Linear 1D convolution. Cyclic 1D convolution. Graph Basics. Graph Matrix Representations. Graph Fourier-like Basis. Graph Signals. Graph Signal Diffusion. Spatial Graph Convolution. Generalizing Convolutions to Graphs. Spectral Graph Convolution. Graph Filtering: Spatial domain, Spectral domain. Spatial – Spectral connection. Graph Signal Sampling. Graph Signals and Stationarity.