Testing Graph Clusterability: Algorithms and Lower Bounds

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generalization of the well-studied problem of testing graph expansion, where one wants to distinguish between the graph having good expansion (i. e. being a good single cluster) and the graph having a sparse cut (i. e. being a union of at least two clusters). A recent work of Czumaj, Peng, and Sohler (STOC'15) gave an ingenious sublinear time algorithm for testing k-clusterability in time Õ(n^1/2 poly(k)). Their algorithm implicitly embeds a random sample of vertices of the graph into Euclidean space, and then clusters the samples based on estimates of Euclidean distances between the points. This yields a very efficient testing algorithm, but only works if the cluster structure is very strong: it is necessary to assume that the gap between conductances of accepted and rejected graphs is at least logarithmic in the size of the graph G. In this paper we show how one can leverage more refined geometric information, namely angles as opposed to distances, to obtain a sublinear time tester that works even when the gap is a sufficiently large constant. Our tester is based on the singular value decomposition of a natural matrix derived from random walk transition probabilities from a small sample of seed nodes. We complement our algorithm with a matching lower bound on the query complexity of testing clusterability. Our lower bound is based on a novel property testing problem, which we analyze using Fourier analytic tools. As a byproduct of our techniques, we also achieve new lower bounds for the problem of approximating MAX-CUT value in sublinear time.

Authors

• Ashish Chiplunkar
• Michael Kapralov
• Sanjeev Khanna
• Aida Mousavifar
• Yuval Peres

Keywords

• Clustering algorithms
• Partitioning algorithms
• Testing
• Graph theory
• Complexity theory
• Approximation algorithms
• Games
• Lower Bound
• Transition Probabilities
• Random Walk
• Cluster Structure
• Vertices
• Testing Algorithm
• Random Probability
• Graph Size
• High Probability
• Proof Of Theorem
• Shortest Path
• Degree Distribution
• Model Configuration
• Transition Probability Matrix
• Problem Instances
• Random Graph
• Clustering Quality
• Vertex Degree
• Graph Properties
• Clustering Problem
• Diagonal Degree Matrix
• Gram Matrix
• Total Variation Distance
• Regular Graphs
• Goal Of This Section
• Input Graph
• Edge Labels
• clustering
• property testing
• sublinear algorithms
• spectral graph theory