Small World and Power Law Phenomena in Networks (original) (raw)

CMPSCI 691S: Scaling, Power Laws, and the Small World Phenomena in Networks

Instructor: Prof. Don Towsley

Time: Tuesdays, 5:10 - 7:10

Location: ELab 325

Computer Science Department

University of Massachusetts

Amherst, Mass. 01003-4610

(413) 545-0207 (office)

(413) 545-1249 (fax)

towsley@cs.umass.edu


Seminar Description

Scaling phenomena permeate modern communication networks. For example, there is considerable data to support the hypothesis that network traffic exhibits multi-scalar behavior, with correlations at multiple timescales. There is even evidence that traffic may be self-similar and fractal in nature. This can be explained by assuming that network workloads are described by power laws. For example, there is considerable evidence that file sizes and web object sizes are described by distributions which decay according to a power law. In other words, if X is the size of the object in bytes, then P[X>x] = c x^{-a}. This may not seem unusual until one realizes that all network models and simulations prior to the early 90s were based on the assumption that P[X>x] = c e^-{ax}.

In this seminar, we will explore different manifestations of scaling phenomena and power laws. Not only do they arise in the context of network traffic characterization, but also in the description of network topologies. This will lead naturally to the "small world" phenomena that has so recently been in the press. During the first third or so of the semester we will focus on scaling in traffic models. We will then look at a very interesting set of papers on control mechanisms that produce power laws. One consequence of the theory in these papers is an explanation of the multiscalr traffic behavior.

The remainder of the course will cover power laws in network topologies, culminating in the small world phenomena. we will explore what it is how it is poorly explained by traditional graph models, and examine preliminary models explaining the phenomena.

In the final weeks of the course, if time permits, we will shift to an entirely diferent topic, namely how to model networks as nonlinear dynamical systems.


Course Prerequisites

A course on networking, a course on probability theory, and reasonable mathematical maturity. Useful references include:


Course Requirements

This course can be taken for one credit or three credits. For one credit, students are expected to attend the course and present one paper. For three credits, students are expected to either turn in detailed critical summaries of all of the papers or to propose and complete a course project.


Reading List and Schedule

Scaling Phenomena in Network Traffic

Power Laws in Control

Power Laws in Networks

The Small World Phenomena

Non-linear Dynamical Systems and Networks


Scaling Phenomena in Network Traffic

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6


Power Laws in Control

Week 7


Power Laws in Networks

Week 8

Week 9


Small World Phenomena

Week 10

Week 11


Nonlinear Dynamical Systems

Week 12