We show a new relationship between various community detection objectives and a correlation clustering framework. These enable us to detect communities with good bounds on the solution. ]]>

We show a new relationship between various community detection objectives and a correlation clustering framework. These enable us to detect communities with good bounds on the solution. ]]>

I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject. ]]>

I presented these slides at the #strathna meeting in Glasgow in June 2017. They are an updated and enhanced version of the earlier talks on the subject. ]]>

A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs. ]]>

A talk I gave at the Park City Institute of Mathematics about our recent work on using motifs to analyze and cluster networks. This involves a higher-order cheeger inequality in terms of motifs. ]]>

My talk at TMA 2016 (The workshop on Tensors, Matrices, and their Applications) on the relationship between a spacey random walk process and tensor eigenvectors]]>

My talk at TMA 2016 (The workshop on Tensors, Matrices, and their Applications) on the relationship between a spacey random walk process and tensor eigenvectors]]>

A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.]]>

A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.]]>

This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on. ]]>

This is my KDD2015 talk on robustness in semi-supervised learning. The paper is already on Michael Mahoney's website: http://www.stat.berkeley.edu/~mmahoney/pubs/robustifying-kdd15.pdf See the KDD paper for all the details, which this talk is a bit light on. ]]>

My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.]]>

My talk at SIAM NetSci workshop (2015) on our new spacey random walk and spacey random surfer models and how we derived them. There many potential extensions and opportunities to use this for analyzing big data as tensors.]]>

Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.]]>

Localized methods in graph mining exploit the local structures in a graph instead attempting to find global structures. These are widely successful at all sorts of problems including community detection, label propagation, and a few others.]]>

A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.]]>

A talk I gave at the SIAM Annual Meeting Mini-symposium on the mathematics of the power grid organized by Mahantesh Halappanavar. I discuss a few ideas on how our dynamic centrality could help analyze such situations.]]>

In a talk at the Institute for Physics and Computational Mathematics in Beijing, I discuss a few different types of structure in iterative methods.]]>

In a talk at the Institute for Physics and Computational Mathematics in Beijing, I discuss a few different types of structure in iterative methods.]]>

In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on. ]]>

In a talk at the Chinese Academic of Sciences Institute for Automation, I discuss some of the MapReduce and community detection methods I've worked on. ]]>

This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic. ]]>

This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic. ]]>

I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.]]>

I describe a few ongoing research projects on diffusions in large graphs and how we can create efficient matrix computations in order to determine them efficiently.]]>

We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.]]>

We study how Google's PageRank method relates to mincut and a particular type of electrical flow in a network. We also explain the details of how the "push method" for computing PageRank helps to accelerate it. This has implications for semi-supervised learning and machine learning, as well as social network analysis.]]>

The matrix exponential is a matrix computing primitive used in link prediction and community detection. We describe a fast method to compute it using relaxation on a large linear system of equations. This enables us to compute a column of the matrix exponential is sublinear time, or under a second on a standard desktop computer.]]>

The matrix exponential is a matrix computing primitive used in link prediction and community detection. We describe a fast method to compute it using relaxation on a large linear system of equations. This enables us to compute a column of the matrix exponential is sublinear time, or under a second on a standard desktop computer.]]>

I gave this talk at Netflix about some of the recent work I've been doing on fast matrix primitives for link prediction and also some non-standard uses of the nuclear norm for ranking.]]>

I gave this talk at Netflix about some of the recent work I've been doing on fast matrix primitives for link prediction and also some non-standard uses of the nuclear norm for ranking.]]>

I discuss some runtimes for the personalized PageRank vector and how it relates to open questions in how we should tackle these network based measures via matrix computations.]]>

I discuss some runtimes for the personalized PageRank vector and how it relates to open questions in how we should tackle these network based measures via matrix computations.]]>

A talk at the SIMONS workshop on Parallel and Distributed Algorithms for Inference and Optimization on how to do tall-and-skinny QR factorizations on MapReduce using a communication avoiding algorithm. ]]>

A talk at the SIMONS workshop on Parallel and Distributed Algorithms for Inference and Optimization on how to do tall-and-skinny QR factorizations on MapReduce using a communication avoiding algorithm. ]]>

A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.]]>

A talk I gave at ancestry.com on Hadoop, SQL, recommendation and graph algorithms. It's a tutorial overview, there are better algorithms than those I describe, but these are a simple starting point.]]>

My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems. ]]>

My talk from the Stanford ICME seminar series on doing network analysis and link prediction using the a fast algorithm for the matrix exponential on graph problems. ]]>

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