Motivated by applications of large-scale graph clustering, we study random-walk-based local algorithms whose running times depend only on the size of the output cluster, rather than the entire graph. All previously known such algorithms guarantee an output conductance o
Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statist...
Graphs are a powerful and expressive means for storing and working with data. As the demand for fas...
Effective resistance is an important metric that measures the similarity of two vertices in a graph....
Given a subset A of vertices of an undirected graph G, the cut-improvement problem asks us to find a...
The problem of graph clustering is a central optimization problem with various applications in numer...
Spectral partitioning is a simple, nearly linear time algorithm to find sparse cuts, and the Cheeger...
Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph a...
Abstract—Clustering of a graph is the task of grouping its nodes in such a way that the nodes within...
A distributed system or network can be modeled as a graph representing the "who knows who" relations...
Graph clustering is an important technique to understand the relationships between the vertices in a...
We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can ...
We consider the problem that on large random geometric graphs, random walk-based distances between n...
Local algorithms on graphs are algorithms that run in par-allel on the nodes of a graph to compute s...
In this paper, we study the problem of testing the conductance of a given graph in the general graph...
I present a bound on the rate of convergence of random walks in graphs that depends upon the conduct...
Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statist...
Graphs are a powerful and expressive means for storing and working with data. As the demand for fas...
Effective resistance is an important metric that measures the similarity of two vertices in a graph....
Given a subset A of vertices of an undirected graph G, the cut-improvement problem asks us to find a...
The problem of graph clustering is a central optimization problem with various applications in numer...
Spectral partitioning is a simple, nearly linear time algorithm to find sparse cuts, and the Cheeger...
Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph a...
Abstract—Clustering of a graph is the task of grouping its nodes in such a way that the nodes within...
A distributed system or network can be modeled as a graph representing the "who knows who" relations...
Graph clustering is an important technique to understand the relationships between the vertices in a...
We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can ...
We consider the problem that on large random geometric graphs, random walk-based distances between n...
Local algorithms on graphs are algorithms that run in par-allel on the nodes of a graph to compute s...
In this paper, we study the problem of testing the conductance of a given graph in the general graph...
I present a bound on the rate of convergence of random walks in graphs that depends upon the conduct...
Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statist...
Graphs are a powerful and expressive means for storing and working with data. As the demand for fas...
Effective resistance is an important metric that measures the similarity of two vertices in a graph....