PPT-WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams

Kijung Shin Triangles in a Graph Graphs are everywhere Social Network Web Emails etc Triangles are a fundamental primitive 3 nodes connected to each other Counting triangles has many applications. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. G.Cormode@warwick.ac.uk. Nick Duffield, Texas A&M University. Nick.Duffield@gmail.com. Sampling for Big Data. x. 9. x. 8. x. 7. x. 6. x. 5. x. 4. x. 3. x. 2. x. 1. x. 10. x’. 9. x’. 8. x’. 10. . Charalampos (Babis) E. Tsourakakis. ctsourak@math.cmu.edu. WAW 2010, Stanford. . 16. th. December ‘10. WAW '10. 1. Joint work . Richard Peng. SCS, CMU. Gary L. Miller . Richard Peng. M.I.T.. OUtline. Structure preserving sampling. Sampling as a recursive ‘driver’. Sampling the inaccessible. What can sampling preserve?. Random Sampling. Collection of many objects. Reference Counting vs. Tracing. Advantages. Immediate. Object-local. Overhead distributed. Very simple . Trivial implementation for naïve RC. Disadvantages. Maintain count . Time and space overheads. of Large Datasets. . Charalampos (Babis) E. Tsourakakis. Brown University. charalampos_tsourakakis@brown.edu. Brown University. of Large Datasets. . Charalampos (Babis) E. Tsourakakis. Brown University. charalampos_tsourakakis@brown.edu. Brown University. From Surveys to Big . D. ata. Edith Cohen. Google Research. Tel Aviv University. Disclaimer:. Random sampling is classic and well studied tool with enormous impact across disciplines. This presentation is biased and limited by its length, my research interests, experience, understanding, and being a Computer Scientist. I will attempt to present some big ideas and selected applications. I hope to increase your appreciation of this incredible tool.. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. Out-of-School Youth. Roy Carr-Hill. Institute of Education, London. roy.carr_hill@yahoo.com. BRIEF. This is a very preliminary attempt to set out the difficulties of Tracking and Testing Out-of-School (henceforth . Eman Ahmad Al-Ghamdi. Sal7a . Hussain. AL-. Montasheri. Areej. Ahmad . khalf. Doa’a. Nashag8i. Ameera. AL.Ghamdi. Ilham. AL.Ghamdi. . Elizabeth Bishop. . 1911-1979. of Global and Local Triangle Counts in Graph Streams. Kijung Shin. 1. Mohammad Hammoud. 1. . Euiwoong. Lee. 1 . . Jinoh. Oh. 2. Christos Faloutsos. 1. 1 . Carnegie Mellon University . 2. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. Accurate Triangle Counting in Graph Streams with Deletions. Kijung Shin. , . Jisu. Kim, Bryan . Hooi. , Christos . Faloutsos. Triangles in a Graph. Accurate Triangle Counting in Graph Streams with Deletions.