Latent Space Models
{"Image DocumentName": "Latent Space Models", "Image ImageDescription": "Map nodes into a low-dimensional space such that the proximity between nodes based on network connectivity is preserved in the new space, then apply k-means clustering. Multi-dimensional scaling (MDS) Given a network, construct a proximity matrix P representing the pairwise distance between nodes (e.g., geodesic distance) Let denote the coordinates of nodes in the low-dimensional space. Objective function: Solution: V is the top eigenvectors of , and is a diagonal matrix of top eigenvalues. \\widetilde{P} is introduced for notational convenience. It can be constructed based on the proximity matrix P.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}