This lecture overviews Web Search based on Ranking that has many applications in Web Science and Social Media Analytics. It covers the following topics in detail: Architecture of Web Search Engine: Crawler, Indexer, Query Processor. Timeline of Ranking at Indexed Pages. Ranking Algorithms: Based on Frequency (TF-IDF), Based on Graph-Link Analysis (PageRank, Hits, Salsa, UsersRank, SimRank). Neural… Continue reading Web Search based on Ranking
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Recommendation Systems
This lecture overviews Recommendation Systems that has many applications in Web Science, Marketing and Social Media Analytics. It covers the following topics in detail: Content Based Filtering. Collaborative Filtering: Memory Based Techniques, Model Based Techniques, Hybrid Techniques. ΚΝΝ algorithm. ALS algorithm. Learning from Implicit Datasets. Matrix Factorization: Funk MF, SVD++, Asymmetric SVD. Hybridization techniques. Deep Learning… Continue reading Recommendation Systems
Information Diffusion
This lecture overviews Information Diffusion that has many applications in Network Theory, Web Science, Political Science, Marketing and Social Media Analytics. It covers the following topics in detail: Basics of Information Diffusion. Social Network Diffusion Models: Ising Model, Epidemic Diffusion Models, Cascade Models, Threshold Models, Game Theory Models, Nature-Inspired Model Influence Models, Influence Models. Applications… Continue reading Information Diffusion
Graph Convolutional Networks
This lecture overviews Graph Convolutional Networks (GCN) that have many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Graph Convolutions. Empirical Risk Minimization with Graph Signals. Learning with Graph Convolutional Filters. Learning with Graph Perceptrons. GCN Types. GCN general architecture. Spectral… Continue reading Graph Convolutional Networks
Graph Neural Networks
This lecture overviews Graph Neural Networks that has many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Introduction to Graphs. Neural Networks. Graph Convolutional Networks (GCN). Recurrent Graph Neural Networks (RGNN). Graph Auto-Encoders. Spatial-Temporal Graph Neural Networks. GNN Applications.
Graph Signal Processing
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.… Continue reading Graph Signal Processing