Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding

no code implementations • 2 Oct 2013 • Vince Lyzinski, Daniel Sussman, Minh Tang, Avanti Athreya, Carey Priebe

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research.

Clustering

Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings

no code implementations • 9 Sep 2021 • Christy Lin, Daniel Sussman, Prakash Ishwar

This paper studies properties of random walk based node embeddings in the unsupervised setting of discovering hidden block structure in the network, i. e., learning node representations whose cluster structure in Euclidean space reflects their adjacency structure within the network.

Link Prediction Node Classification +1