Random Walks and Evolving Sets: Faster Convergences and Limitations
|
|
- Alexandrina Maxwell
- 5 years ago
- Views:
Transcription
1 Random Walks and Evolving Sets: Faster Convergences and Limitations Siu On Chan 1 Tsz Chiu Kwok 2 Lap Chi Lau 2 1 The Chinese University of Hong Kong 2 University of Waterloo Jan 18, /23
2 Finding small clusters Given 1-regular graph with edge weights w (Edge) expansion φ(s) w(s, V \ S) S Task: compute φ δ (G) min S δn φ(s) 2/23
3 Algorithm 1: Random walk For finding small clusters [Spielman Teng 04, Andersen Chung Lang 06, ] Theorem ([Kwok Lau 12]) Random walk returns S with φ(s) = O( φ(s )/ε) and S = O( S 1+ε ) in poly time, when start from nice vertices in S When ε = 1/ log S φ(s) = O( φ(s ) log S ) and S = O( S ) 3/23
4 Analysis: Lovász Simonovits [ 90] Measure progress of lazy random walk mixing with a curve Large φ(g) implies fast mixing (lazy = self-loop of weight 1/2 at every vertex) More on this later 4/23
5 Evolving Set Process [Morris 02] Yields strong bounds on mixing times [Morris Peres 05] Evolving Set Process is Markov Chain {S t } on subsets of V Given S t, choose U uniformly from [0, 1] S t+1 {v V w(s t, v) U} small U S t large U S t+1 5/23
6 Algorithm 2: Evolving Set Process Can find small clusters [Andersen Peres 09, Oveis Gharan Trevisan 12] Theorem ([Oveis Gharan Trevisan 12, AOPT 16]) Evolving Set Process returns S with φ(s) = O( φ(s )/ε) and S = O( S 1+ε ) in poly time, when start from nice vertices in S 6/23
7 Algorithm 2: Evolving Set Process Can find small clusters [Andersen Peres 09, Oveis Gharan Trevisan 12] Theorem ([Oveis Gharan Trevisan 12, AOPT 16]) Evolving Set Process returns S with φ(s) = O( φ(s )/ε) and S = O( S 1+ε ) in poly time, when start from nice vertices in S Conjecture [Oveis Gharan 13, AOPT 16] With non-trivial probability, in fact S = O( S ) If true, will refute Small-Set-Expansion Hypothesis that φ δ (G) is hard to approximate [Raghavendra Steurer 10] (cousin of Unique-Games Conjecture) 6/23
8 Our results: Generalized Lovász Simonovits analysis Also measure random walk mixing with LS curve 1. Large combinatorial gap ϕ(g) implies fast mixing 2. Large robust vertex expansion φ V (G) + laziness imply fast mixing 7/23
9 Our results: Combinatorial gap Large combinatorial gap ϕ(g) implies fast mixing ϕ(g) w(s, T) min 1 S = T n/2 S 1. Similar definition as φ(g) (which only allows T = S) 2. ϕ(g) = φ(g) for lazy graphs 3. ϕ(g) small if G has near-bipartite component Corollary (Expansion of graph powers) φ δ/4 (G t ) = min{ω( tφ δ (G)), 1/20} without laziness assumption of [Kwok Lau 14] 8/23
10 Our results: Vertex expansion Robust vertex expansion φ V defined by [Kannan Lovász Montenegro 06] Theorem Evolving Set Process returns S with φ(s) = O(φ(S )/(εφ V (S))) and S = O( S 1+ε ) in poly time, when start from nice vertices in S Compare: φ(s) = O( φ(s )/ε) in [APOT 16] 9/23
11 Our results: Vertex expansion Robust vertex expansion φ V defined by [Kannan Lovász Montenegro 06] Theorem Evolving Set Process returns S with φ(s) = O(φ(S )/(εφ V (S))) and S = O( S 1+ε ) in poly time, when start from nice vertices in S Compare: φ(s) = O( φ(s )/ε) in [APOT 16] Implies constant factor approximation when φ V (G) min S n/2 φv (S) is Ω(1) Evolving Set Process analog of spectral partitioning result in [Kwok Lau Lee 16] 9/23
12 Our results: Hard instances Theorem For arbitrarily small δ, some graphs have a hidden small cluster S φ(s ) ε and S = δn but Evolving Set Process never returns S with φ(s) 1 ε and S δ ε n Refute the conjecture in [Oveis Gharan 13, AOPT 16] Also limitation of random walk, PageRank, etc 10/23
13 Details: Generalized Lovász Simonovits Analysis 11/23
14 Lovász Simonovits Let p be probability vector Measure random walk mixing with curve C(p, x) C(p, x) sum of first x largest elements in p for integer x C(p, x) C(Ap, x) 12/23
15 Lovász Simonovits Let p be probability vector Measure random walk mixing with curve C(p, x) C(p, x) sum of first x largest elements in p for integer x C(p, x) C(Ap, x) x x(1 φ(g)) x(1 + φ(g)) When lazy C(Ap, x) 1 (C(p, x(1 φ(g))) + C(p, x(1 + φ(g)))) 2 12/23
16 Lovász Simonovits Let p be probability vector Measure random walk mixing with curve C(p, x) C(p, x) sum of first x largest elements in p for integer x C(p, x) C(Ap, x) x x(1 φ(g)) x(1 + φ(g)) When lazy C(Ap, x) 1 (C(p, x(1 φ(g))) + C(p, x(1 + φ(g)))) 2 By induction: C(A t p, x) x n + x (1 φ(g)2 8 ) t 12/23
17 Generalizing Lovász Simonovits x x(1 ϕ(g)) x(1 + ϕ(g)) C(Ap, x) 1 (C(p, x(1 ϕ(g))) + C(p, x(1 + ϕ(g)))) 2 ϕ(g) in place of φ(g) laziness not required ϕ(g) = φ(g) for lazy graphs generalizing LS to non-lazy More intuitive analysis 13/23
18 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 Sort vertices by decreasing d S (i) w(i, S) (Ap)(S) = 0 i n d S (i)p(i) = (d S (i) d S (i + 1)) p(j) 0 i n 1 j i } {{ } C(p,i) 14/23
19 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 Sort vertices by decreasing d S (i) w(i, S) (Ap)(S) = 0 i n d S (i)p(i) = (d S (i) d S (i + 1)) p(j) 0 i n 1 j i } {{ } C(p,i) 14/23
20 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 Sort vertices by decreasing d S (i) w(i, S) (Ap)(S) = 0 i n d S (i)p(i) = (d S (i) d S (i + 1)) p(j) 0 i n 1 j i } {{ } C(p,i) 1 d S (i) 14/23
21 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 Sort vertices by decreasing d S (i) w(i, S) (Ap)(S) = 0 i n d S (i)p(i) = (d S (i) d S (i + 1)) p(j) 0 i n 1 j i } {{ } C(p,i) 1 d S (i) d S (i) d S (i + 1) 14/23
22 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 From earlier (Ap)(S) (d S (i) d S (i + 1))C(p, i) 0 i n 1 d S (i) w(i, S) 1 2 Upper Area 1 S (1 ϕ(g)) 2 15/23
23 Sketch of main ideas C(Ap, S ) 1 (C(p, S (1 ϕ(g))) + C(p, S (1 + ϕ(g)))) 2 From earlier (Ap)(S) (d S (i) d S (i + 1))C(p, i) 0 i n 1 d S (i) w(i, S) 1 2 Upper Area i T d S (i) 1 S (1 ϕ(g)) 2 15/23
24 Details: Hard instances for Evolving Set Process 16/23
25 Hard instances: Noisy hypercube Noisy k-ary hypercube (k = 1/δ) k d vertices, each represented by a string of length d over [k] Transition probability from x { to y: x i When x = x 1 x 2... x d, y i = uniformly from [k] Coordinate cut S = {x x 1 = 0} satisfies φ(s) ε and S = δn prob ε prob 1 ε 17/23
26 Why local algorithms fail Lets start from S 0 = { 0} Evolving Set Process treats all vertices with the same Hamming weight equally The process only explores sets S t that are symmetric under coordinate permutations (in fact, Hamming balls) Small Hamming balls on noisy k-ary hypercube are expanding [Chan Mossel Neeman 14] 18/23
27 Hamming balls B expand B = {x [k] d x r} φ(b) = Pr x y[x, y B] Pr x [x B] 19/23
28 Hamming balls B expand B = {x [k] d x r} φ(b) = Pr x y[x, y B] Pr x [x B] Pr[x B] = E x [1 x xd 0 r] x Pr[g r ] Gaussian g Pr [x, y B] = E x,y 1 xi 0 r and 1 yi 0 r x y 1 i d Pr[g r and h r ] 1 i d Gaussians g, h 19/23
29 Hamming balls B expand B = {x [k] d x r} φ(b) = Pr x y[x, y B] Pr x [x B] 1 i d Pr g,h[g, h r ] Pr g [g r ] Pr[x B] = E x [1 x xd 0 r] x Pr[g r ] Gaussian g Pr [x, y B] = E x,y 1 xi 0 r and 1 yi 0 r x y Pr[g r and h r ] 1 i d Gaussians g, h 19/23
30 Expansion in Gaussian space B = {x [k] d x r} φ(b) = Pr x y[x, y B] Pr x [x B] Pr g,h[g, h r ] Pr g [g r ] r 0 1 r 0 20/23
31 Symmetry Obstacles to all local clustering algorithms Evolving Set Process, random walk, PageRank, etc All fall for the symmetry Folded noisy hypercubes are hard instances for SDP Folding necessary to rule out sparse cuts in those instances In our situation, we cannot fold Our instances are easy for Lasserre/sum-of-squares 21/23
32 Summary New analysis of Lovász Simonovits curve Faster convergence with large combinatorial gap ϕ(g) or robust vertex expansion φ V (G) x x(1 ϕ(g)) x(1 + ϕ(g)) Limitations of all local clustering algorithms: Coordinate cuts vs Hamming balls under symmetry 22/23
33 Open problems Ω( φ(s ) log S ) lower bound for Evolving Set Process and random walk? Thank you 23/23
Graph Partitioning Algorithms and Laplacian Eigenvalues
Graph Partitioning Algorithms and Laplacian Eigenvalues Luca Trevisan Stanford Based on work with Tsz Chiu Kwok, Lap Chi Lau, James Lee, Yin Tat Lee, and Shayan Oveis Gharan spectral graph theory Use linear
More informationLecture 14: Random Walks, Local Graph Clustering, Linear Programming
CSE 521: Design and Analysis of Algorithms I Winter 2017 Lecture 14: Random Walks, Local Graph Clustering, Linear Programming Lecturer: Shayan Oveis Gharan 3/01/17 Scribe: Laura Vonessen Disclaimer: These
More informationMarkov chain methods for small-set expansion
Markov chain methods for small-set expansion Ryan O Donnell David Witmer November 4, 03 Abstract Consider a finite irreducible Markov chain with invariant distribution π. We use the inner product induced
More informationLecture 13: Spectral Graph Theory
CSE 521: Design and Analysis of Algorithms I Winter 2017 Lecture 13: Spectral Graph Theory Lecturer: Shayan Oveis Gharan 11/14/18 Disclaimer: These notes have not been subjected to the usual scrutiny reserved
More informationLOCAL CHEEGER INEQUALITIES AND SPARSE CUTS
LOCAL CHEEGER INEQUALITIES AND SPARSE CUTS CAELAN GARRETT 18.434 FINAL PROJECT CAELAN@MIT.EDU Abstract. When computing on massive graphs, it is not tractable to iterate over the full graph. Local graph
More informationConvergence of Random Walks and Conductance - Draft
Graphs and Networks Lecture 0 Convergence of Random Walks and Conductance - Draft Daniel A. Spielman October, 03 0. Overview I present a bound on the rate of convergence of random walks in graphs that
More informationTesting Cluster Structure of Graphs. Artur Czumaj
Testing Cluster Structure of Graphs Artur Czumaj DIMAP and Department of Computer Science University of Warwick Joint work with Pan Peng and Christian Sohler (TU Dortmund) Dealing with BigData in Graphs
More informationFour graph partitioning algorithms. Fan Chung University of California, San Diego
Four graph partitioning algorithms Fan Chung University of California, San Diego History of graph partitioning NP-hard approximation algorithms Spectral method, Fiedler 73, Folklore Multicommunity flow,
More informationPersonal PageRank and Spilling Paint
Graphs and Networks Lecture 11 Personal PageRank and Spilling Paint Daniel A. Spielman October 7, 2010 11.1 Overview These lecture notes are not complete. The paint spilling metaphor is due to Berkhin
More informationLecture 17: Cheeger s Inequality and the Sparsest Cut Problem
Recent Advances in Approximation Algorithms Spring 2015 Lecture 17: Cheeger s Inequality and the Sparsest Cut Problem Lecturer: Shayan Oveis Gharan June 1st Disclaimer: These notes have not been subjected
More informationapproximation algorithms I
SUM-OF-SQUARES method and approximation algorithms I David Steurer Cornell Cargese Workshop, 201 meta-task encoded as low-degree polynomial in R x example: f(x) = i,j n w ij x i x j 2 given: functions
More informationReductions Between Expansion Problems
Reductions Between Expansion Problems Prasad Raghavendra David Steurer Madhur Tulsiani November 11, 2010 Abstract The Small-Set Expansion Hypothesis (Raghavendra, Steurer, STOC 2010) is a natural hardness
More informationCutting Graphs, Personal PageRank and Spilling Paint
Graphs and Networks Lecture 11 Cutting Graphs, Personal PageRank and Spilling Paint Daniel A. Spielman October 3, 2013 11.1 Disclaimer These notes are not necessarily an accurate representation of what
More informationApproximation & Complexity
Summer school on semidefinite optimization Approximation & Complexity David Steurer Cornell University Part 1 September 6, 2012 Overview Part 1 Unique Games Conjecture & Basic SDP Part 2 SDP Hierarchies:
More informationLecture 12: Introduction to Spectral Graph Theory, Cheeger s inequality
CSE 521: Design and Analysis of Algorithms I Spring 2016 Lecture 12: Introduction to Spectral Graph Theory, Cheeger s inequality Lecturer: Shayan Oveis Gharan May 4th Scribe: Gabriel Cadamuro Disclaimer:
More informationAlgorithmic Primitives for Network Analysis: Through the Lens of the Laplacian Paradigm
Algorithmic Primitives for Network Analysis: Through the Lens of the Laplacian Paradigm Shang-Hua Teng Computer Science, Viterbi School of Engineering USC Massive Data and Massive Graphs 500 billions web
More informationThe Rainbow Turán Problem for Even Cycles
The Rainbow Turán Problem for Even Cycles Shagnik Das University of California, Los Angeles Aug 20, 2012 Joint work with Choongbum Lee and Benny Sudakov Plan 1 Historical Background Turán Problems Colouring
More informationGeometry of Similarity Search
Geometry of Similarity Search Alex Andoni (Columbia University) Find pairs of similar images how should we measure similarity? Naïvely: about n 2 comparisons Can we do better? 2 Measuring similarity 000000
More informationAlgorithms, Graph Theory, and Linear Equa9ons in Laplacians. Daniel A. Spielman Yale University
Algorithms, Graph Theory, and Linear Equa9ons in Laplacians Daniel A. Spielman Yale University Shang- Hua Teng Carter Fussell TPS David Harbater UPenn Pavel Cur9s Todd Knoblock CTY Serge Lang 1927-2005
More informationLower bounds on the size of semidefinite relaxations. David Steurer Cornell
Lower bounds on the size of semidefinite relaxations David Steurer Cornell James R. Lee Washington Prasad Raghavendra Berkeley Institute for Advanced Study, November 2015 overview of results unconditional
More informationlinear programming and approximate constraint satisfaction
linear programming and approximate constraint satisfaction Siu On Chan MSR New England James R. Lee University of Washington Prasad Raghavendra UC Berkeley David Steurer Cornell results MAIN THEOREM: Any
More informationLecture 7. 1 Normalized Adjacency and Laplacian Matrices
ORIE 6334 Spectral Graph Theory September 3, 206 Lecturer: David P. Williamson Lecture 7 Scribe: Sam Gutekunst In this lecture, we introduce normalized adjacency and Laplacian matrices. We state and begin
More informationLecture 1: Contraction Algorithm
CSE 5: Design and Analysis of Algorithms I Spring 06 Lecture : Contraction Algorithm Lecturer: Shayan Oveis Gharan March 8th Scribe: Mohammad Javad Hosseini Disclaimer: These notes have not been subjected
More informationSum-of-Squares Method, Tensor Decomposition, Dictionary Learning
Sum-of-Squares Method, Tensor Decomposition, Dictionary Learning David Steurer Cornell Approximation Algorithms and Hardness, Banff, August 2014 for many problems (e.g., all UG-hard ones): better guarantees
More informationReed-Muller testing and approximating small set expansion & hypergraph coloring
Reed-Muller testing and approximating small set expansion & hypergraph coloring Venkatesan Guruswami Carnegie Mellon University (Spring 14 @ Microsoft Research New England) 2014 Bertinoro Workshop on Sublinear
More informationProbability & Computing
Probability & Computing Stochastic Process time t {X t t 2 T } state space Ω X t 2 state x 2 discrete time: T is countable T = {0,, 2,...} discrete space: Ω is finite or countably infinite X 0,X,X 2,...
More informationHolistic Convergence of Random Walks
Graphs and Networks Lecture 1 Holistic Convergence of Random Walks Daniel A. Spielman October 5, 1 1.1 Overview There are two things that I want to do in this lecture. The first is the holistic proof of
More informationThe Markov Chain Monte Carlo Method
The Markov Chain Monte Carlo Method Idea: define an ergodic Markov chain whose stationary distribution is the desired probability distribution. Let X 0, X 1, X 2,..., X n be the run of the chain. The Markov
More informationApproximate Counting and Markov Chain Monte Carlo
Approximate Counting and Markov Chain Monte Carlo A Randomized Approach Arindam Pal Department of Computer Science and Engineering Indian Institute of Technology Delhi March 18, 2011 April 8, 2011 Arindam
More informationUnique Games Conjecture & Polynomial Optimization. David Steurer
Unique Games Conjecture & Polynomial Optimization David Steurer Newton Institute, Cambridge, July 2013 overview Proof Complexity Approximability Polynomial Optimization Quantum Information computational
More informationSimons Workshop on Approximate Counting, Markov Chains and Phase Transitions: Open Problem Session
Simons Workshop on Approximate Counting, Markov Chains and Phase Transitions: Open Problem Session Scribes: Antonio Blanca, Sarah Cannon, Yumeng Zhang February 4th, 06 Yuval Peres: Simple Random Walk on
More informationSpectral Graph Theory and its Applications. Daniel A. Spielman Dept. of Computer Science Program in Applied Mathematics Yale Unviersity
Spectral Graph Theory and its Applications Daniel A. Spielman Dept. of Computer Science Program in Applied Mathematics Yale Unviersity Outline Adjacency matrix and Laplacian Intuition, spectral graph drawing
More informationLecture: Local Spectral Methods (1 of 4)
Stat260/CS294: Spectral Graph Methods Lecture 18-03/31/2015 Lecture: Local Spectral Methods (1 of 4) Lecturer: Michael Mahoney Scribe: Michael Mahoney Warning: these notes are still very rough. They provide
More informationLecture 15: Expanders
CS 710: Complexity Theory 10/7/011 Lecture 15: Expanders Instructor: Dieter van Melkebeek Scribe: Li-Hsiang Kuo In the last lecture we introduced randomized computation in terms of machines that have access
More informationGraph Clustering Algorithms
PhD Course on Graph Mining Algorithms, Università di Pisa February, 2018 Clustering: Intuition to Formalization Task Partition a graph into natural groups so that the nodes in the same cluster are more
More informationLecture 2: September 8
CS294 Markov Chain Monte Carlo: Foundations & Applications Fall 2009 Lecture 2: September 8 Lecturer: Prof. Alistair Sinclair Scribes: Anand Bhaskar and Anindya De Disclaimer: These notes have not been
More informationCS6999 Probabilistic Methods in Integer Programming Randomized Rounding Andrew D. Smith April 2003
CS6999 Probabilistic Methods in Integer Programming Randomized Rounding April 2003 Overview 2 Background Randomized Rounding Handling Feasibility Derandomization Advanced Techniques Integer Programming
More informationAlgorithms, Graph Theory, and the Solu7on of Laplacian Linear Equa7ons. Daniel A. Spielman Yale University
Algorithms, Graph Theory, and the Solu7on of Laplacian Linear Equa7ons Daniel A. Spielman Yale University Rutgers, Dec 6, 2011 Outline Linear Systems in Laplacian Matrices What? Why? Classic ways to solve
More informationGRAPH PARTITIONING USING SINGLE COMMODITY FLOWS [KRV 06] 1. Preliminaries
GRAPH PARTITIONING USING SINGLE COMMODITY FLOWS [KRV 06] notes by Petar MAYMOUNKOV Theme The algorithmic problem of finding a sparsest cut is related to the combinatorial problem of building expander graphs
More informationStatistical Inference on Large Contingency Tables: Convergence, Testability, Stability. COMPSTAT 2010 Paris, August 23, 2010
Statistical Inference on Large Contingency Tables: Convergence, Testability, Stability Marianna Bolla Institute of Mathematics Budapest University of Technology and Economics marib@math.bme.hu COMPSTAT
More informationLecture 14: SVD, Power method, and Planted Graph problems (+ eigenvalues of random matrices) Lecturer: Sanjeev Arora
princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 14: SVD, Power method, and Planted Graph problems (+ eigenvalues of random matrices) Lecturer: Sanjeev Arora Scribe: Today we continue the
More informationStanford University CS366: Graph Partitioning and Expanders Handout 13 Luca Trevisan March 4, 2013
Stanford University CS366: Graph Partitioning and Expanders Handout 13 Luca Trevisan March 4, 2013 Lecture 13 In which we construct a family of expander graphs. The problem of constructing expander graphs
More informationSpectral Algorithms for Graph Mining and Analysis. Yiannis Kou:s. University of Puerto Rico - Rio Piedras NSF CAREER CCF
Spectral Algorithms for Graph Mining and Analysis Yiannis Kou:s University of Puerto Rico - Rio Piedras NSF CAREER CCF-1149048 The Spectral Lens A graph drawing using Laplacian eigenvectors Outline Basic
More informationLabel Cover Algorithms via the Log-Density Threshold
Label Cover Algorithms via the Log-Density Threshold Jimmy Wu jimmyjwu@stanford.edu June 13, 2017 1 Introduction Since the discovery of the PCP Theorem and its implications for approximation algorithms,
More informationA Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover
A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover Grant Schoenebeck Luca Trevisan Madhur Tulsiani Abstract We study semidefinite programming relaxations of Vertex Cover arising
More informationOn the Logarithmic Calculus and Sidorenko s Conjecture
On the Logarithmic Calculus and Sidorenko s Conjecture by Xiang Li A thesis submitted in conformity with the requirements for the degree of Msc. Mathematics Graduate Department of Mathematics University
More informationSzemerédi s regularity lemma revisited. Lewis Memorial Lecture March 14, Terence Tao (UCLA)
Szemerédi s regularity lemma revisited Lewis Memorial Lecture March 14, 2008 Terence Tao (UCLA) 1 Finding models of large dense graphs Suppose we are given a large dense graph G = (V, E), where V is a
More informationMarkov Chains, Conductance and Mixing Time
Markov Chains, Conductance and Mixing Time Lecturer: Santosh Vempala Scribe: Kevin Zatloukal Markov Chains A random walk is a Markov chain. As before, we have a state space Ω. We also have a set of subsets
More informationSpectral Analysis of Random Walks
Graphs and Networks Lecture 9 Spectral Analysis of Random Walks Daniel A. Spielman September 26, 2013 9.1 Disclaimer These notes are not necessarily an accurate representation of what happened in class.
More informationMaximum cut and related problems
Proof, beliefs, and algorithms through the lens of sum-of-squares 1 Maximum cut and related problems Figure 1: The graph of Renato Paes Leme s friends on the social network Orkut, the partition to top
More informationGraph Partitioning Using Random Walks
Graph Partitioning Using Random Walks A Convex Optimization Perspective Lorenzo Orecchia Computer Science Why Spectral Algorithms for Graph Problems in practice? Simple to implement Can exploit very efficient
More informationFinding is as easy as detecting for quantum walks
Finding is as easy as detecting for quantum walks Jérémie Roland Hari Krovi Frédéric Magniez Maris Ozols LIAFA [ICALP 2010, arxiv:1002.2419] Jérémie Roland (NEC Labs) QIP 2011 1 / 14 Spatial search on
More informationVertex and edge expansion properties for rapid mixing
Vertex and edge expansion properties for rapid mixing Ravi Montenegro Abstract We show a strict hierarchy among various edge and vertex expansion properties of Markov chains. This gives easy proofs of
More informationA local algorithm for finding dense bipartite-like subgraphs
A local algorithm for finding dense bipartite-like subgraphs Pan Peng 1,2 1 State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences 2 School of Information Science
More informationCoupling. 2/3/2010 and 2/5/2010
Coupling 2/3/2010 and 2/5/2010 1 Introduction Consider the move to middle shuffle where a card from the top is placed uniformly at random at a position in the deck. It is easy to see that this Markov Chain
More informationA quantum walk based search algorithm, and its optical realisation
A quantum walk based search algorithm, and its optical realisation Aurél Gábris FJFI, Czech Technical University in Prague with Tamás Kiss and Igor Jex Prague, Budapest Student Colloquium and School on
More informationLecture 5: Random Walks and Markov Chain
Spectral Graph Theory and Applications WS 20/202 Lecture 5: Random Walks and Markov Chain Lecturer: Thomas Sauerwald & He Sun Introduction to Markov Chains Definition 5.. A sequence of random variables
More informationThe coordinates of the vertex of the corresponding parabola are p, q. If a > 0, the parabola opens upward. If a < 0, the parabola opens downward.
Mathematics 10 Page 1 of 8 Quadratic Relations in Vertex Form The expression y ax p q defines a quadratic relation in form. The coordinates of the of the corresponding parabola are p, q. If a > 0, the
More informationDetecting Sharp Drops in PageRank and a Simplified Local Partitioning Algorithm
Detecting Sharp Drops in PageRank and a Simplified Local Partitioning Algorithm Reid Andersen and Fan Chung University of California at San Diego, La Jolla CA 92093, USA, fan@ucsd.edu, http://www.math.ucsd.edu/~fan/,
More informationINTRODUCTION TO MCMC AND PAGERANK. Eric Vigoda Georgia Tech. Lecture for CS 6505
INTRODUCTION TO MCMC AND PAGERANK Eric Vigoda Georgia Tech Lecture for CS 6505 1 MARKOV CHAIN BASICS 2 ERGODICITY 3 WHAT IS THE STATIONARY DISTRIBUTION? 4 PAGERANK 5 MIXING TIME 6 PREVIEW OF FURTHER TOPICS
More informationConductance and Rapidly Mixing Markov Chains
Conductance and Rapidly Mixing Markov Chains Jamie King james.king@uwaterloo.ca March 26, 2003 Abstract Conductance is a measure of a Markov chain that quantifies its tendency to circulate around its states.
More informationarxiv: v4 [cs.ds] 23 Jun 2015
Spectral Concentration and Greedy k-clustering Tamal K. Dey Pan Peng Alfred Rossi Anastasios Sidiropoulos June 25, 2015 arxiv:1404.1008v4 [cs.ds] 23 Jun 2015 Abstract A popular graph clustering method
More informationFrom query complexity to computational complexity (for optimization of submodular functions)
From query complexity to computational complexity (for optimization of submodular functions) Shahar Dobzinski 1 Jan Vondrák 2 1 Cornell University Ithaca, NY 2 IBM Almaden Research Center San Jose, CA
More informationINTRODUCTION TO MCMC AND PAGERANK. Eric Vigoda Georgia Tech. Lecture for CS 6505
INTRODUCTION TO MCMC AND PAGERANK Eric Vigoda Georgia Tech Lecture for CS 6505 1 MARKOV CHAIN BASICS 2 ERGODICITY 3 WHAT IS THE STATIONARY DISTRIBUTION? 4 PAGERANK 5 MIXING TIME 6 PREVIEW OF FURTHER TOPICS
More information1 Adjacency matrix and eigenvalues
CSC 5170: Theory of Computational Complexity Lecture 7 The Chinese University of Hong Kong 1 March 2010 Our objective of study today is the random walk algorithm for deciding if two vertices in an undirected
More informationNot all counting problems are efficiently approximable. We open with a simple example.
Chapter 7 Inapproximability Not all counting problems are efficiently approximable. We open with a simple example. Fact 7.1. Unless RP = NP there can be no FPRAS for the number of Hamilton cycles in a
More informationAn Analysis of Top Trading Cycles in Two-Sided Matching Markets
An Analysis of Top Trading Cycles in Two-Sided Matching Markets Yeon-Koo Che Olivier Tercieux July 30, 2015 Preliminary and incomplete. Abstract We study top trading cycles in a two-sided matching environment
More informationRandomized Algorithms
Randomized Algorithms Prof. Tapio Elomaa tapio.elomaa@tut.fi Course Basics A new 4 credit unit course Part of Theoretical Computer Science courses at the Department of Mathematics There will be 4 hours
More informationDissertation Defense
Clustering Algorithms for Random and Pseudo-random Structures Dissertation Defense Pradipta Mitra 1 1 Department of Computer Science Yale University April 23, 2008 Mitra (Yale University) Dissertation
More informationConvergence of Random Walks
Graphs and Networks Lecture 9 Convergence of Random Walks Daniel A. Spielman September 30, 010 9.1 Overview We begin by reviewing the basics of spectral theory. We then apply this theory to show that lazy
More informationHow Robust are Thresholds for Community Detection?
How Robust are Thresholds for Community Detection? Ankur Moitra (MIT) joint work with Amelia Perry (MIT) and Alex Wein (MIT) Let me tell you a story about the success of belief propagation and statistical
More informationLecture 4: Applications: random trees, determinantal measures and sampling
Lecture 4: Applications: random trees, determinantal measures and sampling Robin Pemantle University of Pennsylvania pemantle@math.upenn.edu Minerva Lectures at Columbia University 09 November, 2016 Sampling
More informationCSE 291: Fourier analysis Chapter 2: Social choice theory
CSE 91: Fourier analysis Chapter : Social choice theory 1 Basic definitions We can view a boolean function f : { 1, 1} n { 1, 1} as a means to aggregate votes in a -outcome election. Common examples are:
More information(K2) For sum of the potential differences around any cycle is equal to zero. r uv resistance of {u, v}. [In other words, V ir.]
Lectures 16-17: Random walks on graphs CSE 525, Winter 2015 Instructor: James R. Lee 1 Random walks Let G V, E) be an undirected graph. The random walk on G is a Markov chain on V that, at each time step,
More informationLecture: Local Spectral Methods (2 of 4) 19 Computing spectral ranking with the push procedure
Stat260/CS294: Spectral Graph Methods Lecture 19-04/02/2015 Lecture: Local Spectral Methods (2 of 4) Lecturer: Michael Mahoney Scribe: Michael Mahoney Warning: these notes are still very rough. They provide
More informationSpectral radius, symmetric and positive matrices
Spectral radius, symmetric and positive matrices Zdeněk Dvořák April 28, 2016 1 Spectral radius Definition 1. The spectral radius of a square matrix A is ρ(a) = max{ λ : λ is an eigenvalue of A}. For an
More informationDiscrete Mathematics. Kernels by monochromatic paths in digraphs with covering number 2
Discrete Mathematics 311 (2011) 1111 1118 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Kernels by monochromatic paths in digraphs with covering
More informationThe Lovász Local Lemma: constructive aspects, stronger variants and the hard core model
The Lovász Local Lemma: constructive aspects, stronger variants and the hard core model Jan Vondrák 1 1 Dept. of Mathematics Stanford University joint work with Nick Harvey (UBC) The Lovász Local Lemma
More informationLecture 9: Counting Matchings
Counting and Sampling Fall 207 Lecture 9: Counting Matchings Lecturer: Shayan Oveis Gharan October 20 Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications.
More informationComplexity Theory of Polynomial-Time Problems
Complexity Theory of Polynomial-Time Problems Lecture 8: (Boolean) Matrix Multiplication Karl Bringmann Recall: Boolean Matrix Multiplication given n n matrices A, B with entries in {0,1} compute matrix
More informationThe Green-Tao theorem and a relative Szemerédi theorem
The Green-Tao theorem and a relative Szemerédi theorem Yufei Zhao Massachusetts Institute of Technology Based on joint work with David Conlon (Oxford) and Jacob Fox (MIT) Green Tao Theorem (arxiv 2004;
More informationApproximating Maximum Constraint Satisfaction Problems
Approximating Maximum Constraint Satisfaction Problems Johan Håstad some slides by Per Austrin Shafi and Silvio celebration, December 10, 2013 The Swedish Angle Today we are celebrating a Turing Award.
More information18.S096: Spectral Clustering and Cheeger s Inequality
18.S096: Spectral Clustering and Cheeger s Inequality Topics in Mathematics of Data Science (Fall 015) Afonso S. Bandeira bandeira@mit.edu http://math.mit.edu/~bandeira October 6, 015 These are lecture
More informationTHE STRUCTURE OF ALMOST ALL GRAPHS IN A HEREDITARY PROPERTY
THE STRUCTURE OF ALMOST ALL GRAPHS IN A HEREDITARY PROPERTY NOGA ALON, JÓZSEF BALOGH, BÉLA BOLLOBÁS, AND ROBERT MORRIS Abstract. A hereditary property of graphs is a collection of graphs which is closed
More informationCOMMUNITY DETECTION IN SPARSE NETWORKS VIA GROTHENDIECK S INEQUALITY
COMMUNITY DETECTION IN SPARSE NETWORKS VIA GROTHENDIECK S INEQUALITY OLIVIER GUÉDON AND ROMAN VERSHYNIN Abstract. We present a simple and flexible method to prove consistency of semidefinite optimization
More informationGraph-Constrained Group Testing
Mahdi Cheraghchi et al. presented by Ying Xuan May 20, 2010 Table of contents 1 Motivation and Problem Definitions 2 3 Correctness Proof 4 Applications call for connected pools detection of congested links
More informationApproximating Sparsest Cut in Graphs of Bounded Treewidth
Approximating Sparsest Cut in Graphs of Bounded Treewidth Eden Chlamtac Weizmann Institute Robert Krauthgamer Weizmann Institute Prasad Raghavendra Microsoft Research New England Abstract We give the first
More informationLow degree almost Boolean functions are sparse juntas
Low degree almost Boolean functions are sparse juntas Irit Dinur Yuval Filmus Prahladh Harsha November 19, 2017 Abstract Nisan and Szegedy showed that low degree Boolean functions are juntas. Kindler and
More informationOn splitting digraphs
On splitting digraphs arxiv:707.03600v [math.co] 0 Apr 08 Donglei Yang a,, Yandong Bai b,, Guanghui Wang a,, Jianliang Wu a, a School of Mathematics, Shandong University, Jinan, 5000, P. R. China b Department
More informationQuasi-randomness is determined by the distribution of copies of a fixed graph in equicardinal large sets
Quasi-randomness is determined by the distribution of copies of a fixed graph in equicardinal large sets Raphael Yuster 1 Department of Mathematics, University of Haifa, Haifa, Israel raphy@math.haifa.ac.il
More informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 8 Luca Trevisan September 19, 2017
U.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 8 Luca Trevisan September 19, 2017 Scribed by Luowen Qian Lecture 8 In which we use spectral techniques to find certificates of unsatisfiability
More informationOn the Dynamic Chromatic Number of Graphs
On the Dynamic Chromatic Number of Graphs Maryam Ghanbari Joint Work with S. Akbari and S. Jahanbekam Sharif University of Technology m_phonix@math.sharif.ir 1. Introduction Let G be a graph. A vertex
More informationSDP Relaxations for MAXCUT
SDP Relaxations for MAXCUT from Random Hyperplanes to Sum-of-Squares Certificates CATS @ UMD March 3, 2017 Ahmed Abdelkader MAXCUT SDP SOS March 3, 2017 1 / 27 Overview 1 MAXCUT, Hardness and UGC 2 LP
More informationGraph Limits: Some Open Problems
Graph Limits: Some Open Problems 1 Introduction Here are some questions from the open problems session that was held during the AIM Workshop Graph and Hypergraph Limits, Palo Alto, August 15-19, 2011.
More informationarxiv: v3 [math.co] 10 Mar 2018
New Bounds for the Acyclic Chromatic Index Anton Bernshteyn University of Illinois at Urbana-Champaign arxiv:1412.6237v3 [math.co] 10 Mar 2018 Abstract An edge coloring of a graph G is called an acyclic
More informationA Schur Complement Cheeger Inequality
A Schur Complement Cheeger Inequality arxiv:8.834v [cs.dm] 27 Nov 28 Aaron Schild University of California, Berkeley EECS aschild@berkeley.edu November 28, 28 Abstract Cheeger s inequality shows that any
More informationPowerful tool for sampling from complicated distributions. Many use Markov chains to model events that arise in nature.
Markov Chains Markov chains: 2SAT: Powerful tool for sampling from complicated distributions rely only on local moves to explore state space. Many use Markov chains to model events that arise in nature.
More informationUnpredictable Walks on the Integers
Unpredictable Walks on the Integers EJ Infeld March 5, 213 Abstract In 1997 Benjamini, Premantle and Peres introduced a construction of a walk on Z that has a faster decaying predictability profile than
More informationThe Green-Tao theorem and a relative Szemerédi theorem
The Green-Tao theorem and a relative Szemerédi theorem Yufei Zhao Massachusetts Institute of Technology Joint work with David Conlon (Oxford) and Jacob Fox (MIT) Simons Institute December 2013 Green Tao
More informationGraph Sparsification III: Ramanujan Graphs, Lifts, and Interlacing Families
Graph Sparsification III: Ramanujan Graphs, Lifts, and Interlacing Families Nikhil Srivastava Microsoft Research India Simons Institute, August 27, 2014 The Last Two Lectures Lecture 1. Every weighted
More information