Task: Cluster the nodes into groups of high similarity based on their multilayer graph connectivity (e.g., each layer refers to one type of relations among a set of common nodes)
Ref: “Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms,” IEEE Transactions on Signal and Information Processing over Networks, 2017
Task: Identify deep communities hidden in networks by effective node or edge removals based on LFVC
Ref: "Deep Community Detection", IEEE Tran. Signal Processing 2015
Ref: "Local Fiedler Vector Centrality for Detection of Deep and Overlapping Communities in Networks" IEEE ICASSP 2014
Task: Computation time comparison of sequential smallest eigenpair between the batch method, the proposed incremental method (Incremental-IO), and the adapted Lanczos algorithm (Lanczos-IO)
Ref: “Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,” Social Network Analysis and Mining, 2018
Task: Simply input a graph (sparse format is preferred) and AMOS will output reliable clustering results via automated clustering selection and multi-stage statistical testing
Ref: "Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering", IEEE Tran. Signal Processing 2018
Ref: "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE ICASSP 2017