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Community detection via random walk
Community detection via random walk
by myesha-ticknor
Draft slides. Background. Consider a social graph...
Random walk
Random walk
by liane-varnes
Presented by Changqing Li. Mathematics. Probabili...
Affinity-Preserving Random Walk for Multi-Document Summarization
Affinity-Preserving Random Walk for Multi-Document Summarization
by eve
Authors: . Kexiang. Wang, . Zhifang. Sui, et al....
BePI : Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart
BePI : Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart
by isla
May 17. BePI: Fast and Memory-Efficient Method for...
Clustering Spatial Data Using Random Walk
Clustering Spatial Data Using Random Walk
by min-jolicoeur
David . Harel. and . Yehuda. . Koren. KDD 2001...
A Random Polynomial-Time Algorithm for Approximating
A Random Polynomial-Time Algorithm for Approximating
by stefany-barnette
the Volume of Convex Bodies. By Group 7. The Prob...
Random Walks
Random Walks
by pamella-moone
and Semi-Supervised Learning. Longin Jan Latecki....
BEAR: B lock E limination
BEAR: B lock E limination
by giovanna-bartolotta
A. pproach. for . R. andom Walk With Restart. on ...
Sampling: an Algorithmic Perspective
Sampling: an Algorithmic Perspective
by jane-oiler
Richard Peng. M.I.T.. OUtline. Structure preservi...
Random Walk with Restart (RWR) for Image Segmentation
Random Walk with Restart (RWR) for Image Segmentation
by stefany-barnette
Sungsu. Lim. AALAB, KAIST. Image Segmentation. C...
Sampling from Gaussian Graphical Models via Spectral Sparsi
Sampling from Gaussian Graphical Models via Spectral Sparsi
by briana-ranney
Richard Peng. M.I.T.. Joint work with . Dehua. C...
Sampling from Gaussian Graphical Models via Spectral Sparsi
Sampling from Gaussian Graphical Models via Spectral Sparsi
by briana-ranney
Richard Peng. M.I.T.. Joint work with . Dehua. C...
BEAR: B lock E limination
BEAR: B lock E limination
by phoebe-click
A. pproach. for . R. andom Walk With Restart. on ...
“Data is the oil of the new age”
“Data is the oil of the new age”
by sadie
“Data is the oil of the new age”. “Data is t...
A Random Act of Kindness
A Random Act of Kindness
by calandra-battersby
Reflection week beginning 23 May 2016. A Random A...
Sampling and Volume Computation in High Dimension
Sampling and Volume Computation in High Dimension
by debby-jeon
Santosh . Vempala. Tutorial outline. Intro to hig...
Sampling and Volume Computation in High Dimension
Sampling and Volume Computation in High Dimension
by kittie-lecroy
Santosh . Vempala. Tutorial outline. Intro to hig...
Superdiffusive Dispersion and Mixing of Swarms with Reactiv
Superdiffusive Dispersion and Mixing of Swarms with Reactiv
by liane-varnes
Jacob Beal. IEEE SASO. September, 2013. Scale-fre...
Testing
Testing
by tawny-fly
. the. Cluster . Structure. . of. Graphs. Chr...
Analysis of Network Diffusion
Analysis of Network Diffusion
by lindy-dunigan
and. Distributed Network Algorithms. Rajmohan Raj...
A Monte Carlo Algorithm for Cold Start Recommendation
A Monte Carlo Algorithm for Cold Start Recommendation
by lois-ondreau
1. Authors: Yu . Rong. , . Xio. Wen, Hong Cheng....
Science Seminar Presentation
Science Seminar Presentation
by briana-ranney
Yana Kortsarts. Computer Science. Research in Com...
Discrepancy and SDPs
Discrepancy and SDPs
by marina-yarberry
Nikhil . Bansal. . (TU Eindhoven, Netherlands )....
Discrepancy and SDPs
Discrepancy and SDPs
by ellena-manuel
Nikhil Bansal (TU Eindhoven). Outline. Discrepanc...
Statistics and Data Analysis
Statistics and Data Analysis
by debby-jeon
Professor William Greene. Stern School of Busines...
More on Rankings
More on Rankings
by cheryl-pisano
Query-independent LAR. Have an a-priori ordering ...
Discrepancy and SDPs
Discrepancy and SDPs
by phoebe-click
Nikhil Bansal (TU Eindhoven). Outline. Discrepanc...
Maze Solving Algorithms
Maze Solving Algorithms
by lois-ondreau
Raman Veerappan. EPS 109 Final Project. Introduct...
A very common deposition model: snow particles falling on a
A very common deposition model: snow particles falling on a
by sherrill-nordquist
Introduction. . to. . fractal. . conceps. “S...
Time Series Econometrics:
Time Series Econometrics:
by lois-ondreau
Some Basic Concepts. Reference : Gujarati, Chapte...
A New Approach of Finding the Steady-State Visit Rates of a
A New Approach of Finding the Steady-State Visit Rates of a
by ellena-manuel
Kurtis. Cahill . James Badal. Introduction. Mode...
Query Suggestion
Query Suggestion
by olivia-moreira
Naama. Kraus. Slides are based on the papers:. B...
Discrepancy and SDPs Nikhil Bansal (TU Eindhoven)
Discrepancy and SDPs Nikhil Bansal (TU Eindhoven)
by dorothy
Outline. Discrepancy: definitions and applications...
Lecture 13 Introduction to Stochastic Processes: Hurst Exponent
Lecture 13 Introduction to Stochastic Processes: Hurst Exponent
by erica
John Rundle . Econophysics. PHYS 250. Stochastic ...
Estimating Clustering Coefficients and Size of Social Networks via Random Walk
Estimating Clustering Coefficients and Size of Social Networks via Random Walk
by leventiser
Stephen J. . Hardiman. *. Capital Fund Management ...
Time Series Econometrics:
Time Series Econometrics:
by alexa-scheidler
Some Basic Concepts. Reference : Gujarati, Chapte...
Sparsified Matrix Algorithms for Graph Laplacians
Sparsified Matrix Algorithms for Graph Laplacians
by conchita-marotz
Richard Peng. Georgia Tech. OUtline. (Structured)...
Sparsified Matrix Algorithms for Graph Laplacians
Sparsified Matrix Algorithms for Graph Laplacians
by lois-ondreau
Richard Peng. Georgia Tech. OUtline. (Structured)...
Classical (and Useful) Markov Chains
Classical (and Useful) Markov Chains
by lindy-dunigan
Markov Chains Seminar, 9.11.2016. Tomer Haimovich...
Semi-Supervised Classification of Network Data Using Very Few Labels
Semi-Supervised Classification of Network Data Using Very Few Labels
by marina-yarberry
Frank Lin and William W. Cohen. School of Compute...