Spectral methods for testing cluster structure of graphs

In the framework of graph property testing, we study the problem of determining if a graph admits a cluster structure. We say that a graph is $(k, φ)$-clusterable if it can be partitioned into at most $k$ parts such that each part has conductance at least $φ$. We present an algorithm that accepts all graphs that are $(2, φ)$-clusterable with probability at least $\frac{2}3$ and rejects all graphs that are $ε$-far from $(2, φ^*)$-clusterable for $φ^* \le μφ^2 ε^2$ with probability at least $\frac{2}3$ where $μ> 0$ is a parameter that affects the query complexity. This improves upon the work of Czumaj, Peng, and Sohler by removing a $\log n$ factor from the denominator of the bound on $φ^*$ for the case of $k=2$. Our work was concurrent with the work of Chiplunkar et al.\@ who achieved the same improvement for all values of $k$. Our approach for the case $k=2$ relies on the geometric structure of the eigenvectors of the graph Laplacian and results in an algorithm with query complexity $O(n^{1/2+O(1)μ} \cdot \text{poly}(1/ε, 1/φ,\log n))$.