49 citations · 112 across the 6 of their papers we have counts for
9 papers
Geometric Bounds on the Fastest Mixing Markov Chain
Sam Olesker-Taylor, Luca Zanetti
In the Fastest Mixing Markov Chain problem, we are given a graph and desire the discrete-time Markov chain with smallest mixing time subject to having equilibrium…
Random walks on randomly evolving graphs
Leran Cai, Thomas Sauerwald, Luca Zanetti
A random walk is a basic stochastic process on graphs and a key primitive in the design of distributed algorithms. One of the most important features of random walks is that, under…
Hermitian matrices for clustering directed graphs: insights and applications
Mihai Cucuringu, Huan Li, He Sun +1
Graph clustering is a basic technique in machine learning, and has widespread applications in different domains. While spectral techniques have been successfully applied for cluste…
Random Walks on Dynamic Graphs: Mixing Times, HittingTimes, and Return Probabilities
Thomas Sauerwald, Luca Zanetti
We establish and generalise several bounds for various random walk quantities including the mixing time and the maximum hitting time. Unlike previous analyses, our derivations are…
Hermitian Laplacians and a Cheeger inequality for the Max-2-Lin problem
Huan Li, He Sun, Luca Zanetti
We study spectral approaches for the MAX-2-LIN(k) problem, in which we are given a system of linear equations of the form , and required to find…
Distributed Graph Clustering and Sparsification
He Sun, Luca Zanetti
Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the…
Distributed Graph Clustering by Load Balancing
He Sun, Luca Zanetti
Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the…
Partitioning Well-Clustered Graphs: Spectral Clustering Works!
Richard Peng, He Sun, Luca Zanetti
In this paper we study variants of the widely used spectral clustering that partitions a graph into k clusters by (1) embedding the vertices of a graph into a low-dimensional space…
A quantum-walk-inspired adiabatic algorithm for graph isomorphism
Dario Tamascelli, Luca Zanetti
We present a 2-local quantum algorithm for graph isomorphism GI based on an adiabatic protocol. By exploiting continuous-time quantum-walks, we are able to avoid a mere diffusion o…