1. PERTEMUAN 26

2. Markov Chains and Random Walks

3. Fundamental Theorem of Markov Chains If M g is an irreducible, aperiodic Markov Chain: 1. All states are positive reccurent. 2. P k converges to W, where each row of W is the same (and equal to , say) 3.  is the unique vector for which  P = 

4. Fundamental Theorem of Markov Chains Let M g be a Markov Chain with states S 0 …S n The Fundamental Theorem tells us that after a sufficiently large number of time steps, the probability of being in state S i+1 is the same as being in state S i. This steady-state condition is known as a stationary distribution The rate at which a Markov Chain converges to a stationary distribution is called the mixing rate.

5. Random Walks A Random Walk on connected, undirected, non-bipartite Graph G can be modeled as a Markov Chain M g, where the vertices of the Graph, V(G), are represented by the states of the the Markov Chain and the transition matrix is as follows If (u,v) is a member of E otherwise

6. Random Walks M g is irreducible because G is connected M g is aperiodic Periodicity is the GCD of the length of all closed walks on G Since G is undirected, there exist closed walks of length 2 (u,v  E, exists walk u-v-u) Since G is non-bipartite it contains odd cycles Therefore GCD of all closed walks is 1 M g is aperiodic

7. Random Walks Given that M g is aperiodic and irreducible, we can apply the Fundamental Theorem of Markov Chains and deduce that M g converges to a stationary distribution. Lemma: For all v  V,  v = d(v) / 2 |E| ( d(v) = the degree of v) Proof denote the component corresponding to vertex v in the probability vector

8. Hitting time (h uv ) – expected number of steps in a Random Walk that starts at u and ends upon its first visit to v Commute time (c uv ) -- expected number of steps in a Random Walk that starts at u, visitsv once and returns to u. (c uv = h uv + h vu )

9. The Lollipop Graph Lollipop Graph consists of n vertices A clique on n/2 vertices A path on n/2 vertices Let u,v  V, u is in the clique, v is at the far end of the path. Surprisingly, h uv != h vu ( h uv is  (n 3 ) h vu is  (n 2 )

10. Markov Chains: an Application Link Prediction and Path Analysis using Markov Chains Use Markov Chains to perform probabilistic analysis and modeling of weblink sequences; ie. If a user requests page n, what will be her most likely next choice Possible Applications  Web Server Request Prediction  Adaptive Web Navigation  Tour Generation  Personalized Hub Model can be used in adaptive mode; transition matrix can be updated as new data (example: Web Server Request) arrives

11. Markov Chains: an Application Link Prediction and Path Analysis using Markov Chains System Overview

12. Markov Chains: an Application Experimental Results HTTP Server Request Prediction 6572 URIs (including html documents, directories, gifs, and cgi requests) 40,000 Requests Over 50% of the web server requests can be predicted to be the state with the highest probability

13. References L. Lovasz. Random Walks on Graphs: A Survey. Combinatorics: Paul Erdos is Eighty (vol. 2), 1996, pp. 353-398. (http://www.cs.yale.edu/HTML/YALE/CS/HyPlans/lovasz/erdos.ps)http://www.cs.yale.edu/HTML/YALE/CS/HyPlans/lovasz/erdos.ps R. Sarukkai, "Link Prediction and Path Analysis Using Markov Chains: 9th World Wide Wide Conference, May, 2000. (http://www9.org/w9cdrom/68/68.html)http://www9.org/w9cdrom/68/68.html Introduction to Markov chains : with special emphasis on rapid mixing / Ehrhard Behrends. Germany [1990-onward] Vieweg & Sohn, GW Am Math Soc 2000.