Proof of Kleinberg’s small-world theorems

Slides:



Advertisements
Similar presentations
Routing Complexity of Faulty Networks Omer Angel Itai Benjamini Eran Ofek Udi Wieder The Weizmann Institute of Science.
Advertisements

Routing in Poisson small-world networks A. J. Ganesh Microsoft Research, Cambridge Joint work with Moez Draief.
The Small World Phenomenon: An Algorithmic Perspective Speaker: Bradford Greening, Jr. Rutgers University – Camden.
1 Analyzing Kleinberg’s Small-world Model Chip Martel and Van Nguyen Computer Science Department; University of California at Davis.
Small-world networks.
Online Social Networks and Media Navigation in a small world.
Social Networks 101 P ROF. J ASON H ARTLINE AND P ROF. N ICOLE I MMORLICA.
Rumors and Routes Rajmohan Rajaraman Northeastern University, Boston May 2012 Chennai Network Optimization WorkshopRumors and Routes1.
Algorithmic and Economic Aspects of Networks Nicole Immorlica.
CPSC 689: Discrete Algorithms for Mobile and Wireless Systems Spring 2009 Prof. Jennifer Welch.
Information Networks Small World Networks Lecture 5.
Lecture 7 CS 728 Searchable Networks. Errata: Differences between Copying and Preferential Attachment In generative model: let p k be fraction of nodes.
School of Information University of Michigan SI 614 Search in structured networks Lecture 15.
On the Spread of Viruses on the Internet Noam Berger Joint work with C. Borgs, J.T. Chayes and A. Saberi.
ETH Zurich – Distributed Computing Group Roger Wattenhofer 1ETH Zurich – Distributed Computing – Christoph Lenzen Roger Wattenhofer Exponential.
1 Analyzing Kleinberg’s (and other) Small-world Models Chip Martel and Van Nguyen Computer Science Department; University of California at Davis.
CSE 522 – Algorithmic and Economic Aspects of the Internet Instructors: Nicole Immorlica Mohammad Mahdian.
Fault-tolerant Routing in Peer-to-Peer Systems James Aspnes Zoë Diamadi Gauri Shah Yale University PODC 2002.
Building Low-Diameter P2P Networks Eli Upfal Department of Computer Science Brown University Joint work with Gopal Pandurangan and Prabhakar Raghavan.
Ecs289m Spring, 2008 Social Network Models S. Felix Wu Computer Science Department University of California, Davis
A Note on Finding the Nearest Neighbor in Growth-Restricted Metrics Kirsten Hildrum John Kubiatowicz Sean Ma Satish Rao.
The Small World Phenomenon: An Algorithmic Perspective by Anton Karatoun.
1 Analyzing Kleinberg’s (and other) Small-world Models Chip Martel and Van Nguyen Computer Science Department; University of California at Davis.
Maximal Independent Set Distributed Algorithms for Multi-Agent Networks Instructor: K. Sinan YILDIRIM.
Decomposing Networks and Polya Urns with the Power of Choice Joint work with Christos Amanatidis, Richard Karp, Christos Papadimitriou, Martha Sideri Presented.
It’s a Small World After All Kim Dressel - The small world phenomenon Please hold applause until the end of the presentation. Angie Heimkes Eric Larson.
The Science of Networks 6.1 Overview Social Goal. Explain why information and disease spread so quickly in social networks. Mathematical Approach. Model.
Proof of Kleinberg’s small-world theorems
Basics Set systems: (X,F) where F is a collection of subsets of X. e.g. (R 2, set of half-planes) µ: a probability measure on X e.g. area/volume is a.
Distributed Coloring Discrete Mathematics and Algorithms Seminar Melih Onus November
Small-world networks. What is it? Everyone talks about the small world phenomenon, but truly what is it? There are three landmark papers: Stanley Milgram.
Using the Small-World Model to Improve Freenet Performance Hui Zhang Ashish Goel Ramesh Govindan USC.
Searching via Your Neighbor’s Neighbor: The Power of Lookahead in P2P Networks Moni Naor Udi Wieder The Weizmann Institute of Science Gurmeet Manku Stanford.
CCAN: Cache-based CAN Using the Small World Model Shanghai Jiaotong University Internet Computing R&D Center.
HEINZ NIXDORF INSTITUTE University of Paderborn Algorithms and Complexity Algorithms for Radio Networks Exercise 11 Stefan Rührup
Gennaro Cordasco - How Much Independent Should Individual Contacts be to Form a Small-World? - 19/12/2006 How Much Independent Should Individual Contacts.
Online Social Networks and Media
Computational Geometry Piyush Kumar (Lecture 10: Point Location) Welcome to CIS5930.
Navigation in small worlds Social Networks: Models and Applications Seminar Toronto, Fall 2007 (based on a presentation by Stratis Ioannidis)
Trade-offs Between Mobility and Density for Coverage in Wireless Sensor Networks Wei Wang, Vikram Srinivasan and Kee-Chaing Chua National University of.
Chord Advanced issues. Analysis Theorem. Search takes O (log N) time (Note that in general, 2 m may be much larger than N) Proof. After log N forwarding.
1 Analyzing and Characterizing Small-World Graphs Van Nguyen and Chip Martel Computer Science, UC Davis.
Chord Advanced issues. Analysis Search takes O(log(N)) time –Proof 1 (intuition): At each step, distance between query and peer hosting the object reduces.
Siddhartha Gunda Sorabh Hamirwasia.  Generating small world network model.  Optimal network property for decentralized search.  Variation in epidemic.
1 HEINZ NIXDORF INSTITUTE University of Paderborn Algorithms and Complexity Christian Schindelhauer Algorithms for Radio Networks Winter Term 2005/2006.
1 Distributed Vertex Coloring. 2 Vertex Coloring: each vertex is assigned a color.
Models and Algorithms for Complex Networks
Randomized Algorithms for Distributed Agreement Problems Peter Robinson.
Small-world phenomenon: An Algorithmic Perspective Jon Kleinberg.
Peer-to-Peer Networks 07 Degree Optimal Networks
Algorithms For Small World Networks Anurag singh.
An Introduction to Computational Geometry
Minimum Spanning Tree 8/7/2018 4:26 AM
Peer-to-Peer Networks 07 Degree Optimal Networks
Know thy Neighbor’s Neighbor Better Routing for Skip Graphs and Small Worlds Moni Naor Udi Wieder.
Peer-to-Peer and Social Networks
SKIP GRAPHS James Aspnes Gauri Shah SODA 2003.
Milgram’s experiment really demonstrated two striking facts about large social networks: first, that short paths are there in abundance;
Maximal Independent Set
The Art Gallery Problem
TexPoint fonts used in EMF.
Chord Advanced issues.
SKIP LIST & SKIP GRAPH James Aspnes Gauri Shah
Chord Advanced issues.
Introduction Wireless Ad-Hoc Network
Chord Advanced issues.
Peer-to-Peer Networks 08 Kelips and Epidemic Algorithms
Lecture 21 More Approximation Algorithms
Navigation and Propagation in Networks
SKIP GRAPHS (continued)
Presentation transcript:

Proof of Kleinberg’s small-world theorems

Kleinberg’s Small-World Model Consider an (n x n) grid. Each node has links to every node at lattice distance p (short range neighbors) & q long range links. Choose long-range links s.t. the prob. to have a long range contact at lattice distance d is proportional to 1/dr n p = 1, q = 2 r = 2 n Recall Kleinberg’s results.Is there a justification?

Results Theorem 1 There is a constant (depending on p and q but independent of n), so that when r = 0, the expected delivery time of any decentralized algorithm is at least

Observation 4. j How many nodes are at a lattice distance j from a given node? 4. j How many nodes are at a lattice distance j or less from a given node? 1 + 4.j + 4. (j-1) + 4. (j-2) + … = 1 + 4. j. (j+1)/2 = 1 + 2j(j+1)

Proof of theorem 1 U .. u v Probability that the source u will lie outside U = (n2 - 2. n 4/3) / n2 = 1 - 2/n2/3 (w.h.p) n2/3 Probability that a node outside U has a long distance link inside U = 2. n 4/3 / n2 = 2/n -2/3 . So, roughly in O(n2/3) steps, the query will Enter U, and thereafter, it can take at most n2/3 steps.

Results Theorem 2. There is a decentralized algorithm A and a constant dependent on p and q but independent of n, so that when r = 2 and p = q = 1, the expected delivery time of A is at most

Proof of theorem 2 Phase j means Distance from t is between 2j and 2j+1 21 20 target v 22 source u

Proof Main idea We show that in phase j, the expected time before the current message holder has a long-range contact within lattice distance 2j of t is bounded proportionally to log n; at this point, phase j will come to an end. As there are at most log n phases, a bound proportional to log2n follows.

Proof Probability (u chooses v as its long-range contact) is But There are 4j nodes at distance j But Thus, the probability that v is chosen is <

Proof Phase j  2j+1 ≤ (distance to v) < 2j Ball Bj consists Of all nodes within Lattice distance 2j from the target Phase j  2j+1 ≤ (distance to v) < 2j The maximum value of j is log n. When will phase j end? What is the prob that it will end in the next step? v No of nodes in Ball Bj is u each within distance 2j+1 + 2j < 2j+1 from a node like u

Proof So each has a probability of Ball Bj consists Of all nodes within Lattice distance 2j from the target So each has a probability of being a long-distance contact of u, So, the search enters Bj with a probability of at least v u So, the expected number of steps spent in phase j is 128 ln (6n). Since There are at most log n phases, the Expected time to reach v is O(log n)2