Corpus ID: 10267277

Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models

  title={Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models},
  author={Amin Jalali and Qiyang Han and Ioana Dumitriu and M. Fazel},
The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities. Despite the recent burst of interest in community detection under the SBM from statistical and computational points of view, there are still gaps in understanding the fundamental limits of recovery. In this paper, we consider the SBM in its full generality, where there is no restriction on the number and sizes of communities or how they grow with the number of nodes, as well as on the… Expand
Scalable Community Detection In The Heterogeneous Stochastic Block Model
  • Andre Beckus, George K. Atia
  • Computer Science
  • 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
  • 2019
Exact Recovery in the Hypergraph Stochastic Block Model: a Spectral Algorithm
Scalable and Robust Community Detection With Randomized Sketching
Sketch-based Community Detection via Representative Node Sampling


Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
Consistency Thresholds for the Planted Bisection Model
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices
Community Detection via Random and Adaptive Sampling
Community detection in sparse networks via Grothendieck’s inequality
Spectral partitioning of random graphs
  • F. McSherry
  • Computer Science
  • Proceedings 2001 IEEE International Conference on Cluster Computing
  • 2001
Breaking the Small Cluster Barrier of Graph Clustering
Robust and Computationally Feasible Community Detection in the Presence of Arbitrary Outlier Nodes