PPT-Sublinear Algorithms for Personalized PageRank, with Applications

Ashish Goel Joint work with Peter Lofgren Sid Banerjee C Seshadhri 1 Personalized PageRank 2 Assume a directed graph with n nodes and m edges Motivation Personalized Search 3 Motivation Personalized Search. acjp takiba yiwata issutokyoacjp ABSTRACT We propose a new scalable algorithm that can compute Per sonalizedPageRankPPRveryquickly ThePowermethod is a stateoftheart algorithm for computing exact PPR however it requires many iterations Thus reducing vision. Applications and Algorithms in CV. Tutorial . 9:. Descriptors. Visual Descriptors. Motivation::. Scene Classification. I. ntroduction. How to differentiate between scenes?. Applications and Algorithms in CV. CS2HS Workshop. Google. Google’s . Pagerank. algorithm is a marvel in terms of its effectiveness and simplicity.. The first company whose initial success was entirely due to “discovery/invention” of a clever algorithm.. Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . Huijia. Lin (USB), . Rafael Pass . (Cornell). Karn. . Seth . (Cornell -> Google). Sid . Telang. (Cornell -> Google). IO. Plethora of Applications. For example: SW14, BCP14, BZ14, GGHR14, BGL. Dana Ron . Tel-Aviv University. ADGA, October 2015. Efficient (Centralized) . Algorithms. Usually, when we say that an algorithm is . efficient . we mean that it runs in time . polynomial. in the input size . Hubs and Authorities (HITS). Combatting Web Spam. Dealing with Non-Main-Memory Web Graphs. Jeffrey D. Ullman. Stanford University. HITS. Hubs. Authorities. Solving the Implied Recursion. 3. Hubs and . NIbble. 2. Why I’m talking about graphs. Lots of large data . is . graphs. Facebook, Twitter, citation data, and other . social. networks. The web, the blogosphere, the semantic web, Freebase, . W. Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. COMS E6998-9. . F15. Administrivia. , Plan. Admin:. My office hours after class (CSB517). Plan:. Finalize . embeddings. Sublinear-time algorithms. Projects. Scriber?. 2. Embeddings of various metrics . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Graph Algorithms Adapted from UMD Jimmy Lin’s slides, which is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for Graph algorithms . A prototypical graph algorithm: PageRank. In memory. Putting more and more on disk …. Sampling from a graph. What is a good sample? (. graph statistics. ). What methods work? (PPR/RWR). William Cohen. Outline. Last week:. PageRank – one sample algorithm on graphs. edges and nodes in memory. nodes in memory. nothing in memory. Aapo. Properties of (social) graphs. GraphLab. /. Powergraph.