The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank
diffusion, is useful in identifying a community nearby a starting seed node. We present the …

We review the recent use of functions of matrices in the analysis of graphs and networks,
with special focus on centrality and communicability measures and diffusion processes. Both …

We present a new class of fast polylog-linear algorithms based on the theory of structured
matrices (in particular low displacement rank) for integrating tensor fields defined on …

Heat kernel pagerank is a variation of Personalized PageRank given in an exponential
formulation. In this work, we present a sublinear time algorithm for approximating the heat …

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank,
Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and …

We study the complexity of local graph-centrality estimation, with the goal of approximating
the centrality score of a given target node while exploring only a sublinear number of …

We consider stochastic transition matrices from large social and information networks. For
these matrices, we describe and evaluate three fast methods to estimate one column of the …

In this paper, we propose a control law in an active disturbance rejection control (ADRC) for
removal of ramp disturbance. We use plant inverse characteristics as a control law …

Graphs are central tools to aid our understanding of biological, physical, and social systems.
Graphs also play a key role in representing and understanding the visual world around us …