1. Graph Data Mining with Map-Reduce Nima Sarshar, Ph.D. INTUIT Inc, Nima_sarshar@intuit.com

2. Intuit, Graphs and Me Me: Large-scale graph data processing, complex networks analysis, graph algorithms … Intuit: QuickBooks, TurboTax, Mint.com, GoPayment, … Graphs @ Intuit: Commercial Graph is the business social network 2 B1 B2C1

3. My Goals for this Talk You leave with your inner computer scientist tantalized: There is more to writing efficient Map-Reduce algorithms than counting words and merging logs You get a general sense of the state of the research I convince you of the need for a real graph processing package for Hadoop You know a bit about our work at Intuit

4. Plan Jump right to it with an example (enumerating triangles) Define the performance metrics (what are we optimizing for?) Give a classification of known recipes The triangle example with with a new trick Personalized PageRank, connected components A list of other algorithms 4

5. Finding Triangles with Map-Reduce 12 3 4 13232434 3 4 4 3 2 2 2 4 3 1 1 3 5 Potential Triangles to Consider Another round of Map Reduce jobs will check for the existence of the closing edge

6. Problems with this Approach 1.Each triangle will be detected 3 times – once under each of its 3 vertices 2.Too many potential triangles are created in the first reduce step. For a node with degree d: Total # of records: 6

7. Modified Algorithm [Cohen 08] 12 3 4 13232434 3 4 2 4 3 1 3 For each triangle exactly one potential triangle is created (under the lowest value node)

8. The quadratic problem still persists This is neat. At least we are not triple counting But the quadratic problem still exists. The number of records is still O(N ) We want to avoid binning edges under high degree nodes The ordering of nodes is arbitrary! Let the degree of a node define its order. 8 Bin an edge under its LOW DEGREE node Break ties arbitrarily, but consistently 32 1 4 5 14 5 3 2

9. The performance Worst case: records vs. The same as the best serial algorithm [Suri 11] The gain for real graphs is fairly substantial. If a graph is reasonably random, it cuts down to: vs. For a heavy-tailed social graph (like our Commercial Graph), this can be fairly huge 9

10. Enumerating Rectangles Triangles will tell you the friends you have in common with another friend People you May Know: Find another node, not connected to you, who has many friends in common with you. That node is a good candidate for friendship. Basis of User Based or Content Based collaborative filtering If the graph is bi-partite 10

11. Generalization to Rectangles 11 There are 4 classes for a rectangle: requires a bit more work 2 3 4 1 3 2 4 1 2 4 3 1 A BC Ordering triangle nodes has a unique equivalency class

12. Performance Metrics Computation: Total computation in all mappers and reducers Communication: How many bits are shuffled from the mapper to the reducer Number of map-reduce steps: You can work it into the above The overhead of running jobs 12

13. Recipes for Graph MR Algorithms Roughly two classes of algorithms: 1.Partition-Compute then Merge Create smaller sub-graphs that fit into a single memory Do computation on the small graphs Construct the final answer from the answers to the small sub-problems 2.Compute-in-Parallel then Merge 13

14. Partition-Compute-Merge 14

15. Finding Triangles By Partitioning [Suri 11] 1.Partition the nodes into b sets: 2.For every 3 sets create a reducer. 3.Send an edge to iff both its ends are in 4.Detect triangles using a serial algorithm within each reducer 15

16. b=4, V 1 ={1}, V 2 ={2}, V 3 ={3}, V 4 ={4}, 12 3 4 13232434 V 1,2,3 V 1,3,4 V 2,3,4 3 42 3 4 3 1 2 1

17. Analysis Every triangle is detected. All 3 vertices are guaranteed to be in at least one partition Average # edges in each reducer is Use an optimal serial triangle finder at each reducer. The total amount of work at all reducers is: # of edges sent from the mappers to reducers (communication cost) is 17

18. One Problem Each triangle may be detected multiple times. If all three vertices are mapped to the same partition, it will be detected times This can be fixed with a similar ordering-of-nodes trick [Afrati12] Can be generalized to detect other small graph structures efficiently [Afrati 12] 18

19. Minimum Weights Spanning Tree 1.Partition the nodes into b sets 2.For every pair of sets create a reducer 3.Send all edges that have both their ends in one pair to the corresponding reducer 4.Compute the minimum spanning tree for the graph in each reducer. Remove other edges to sparsify the graph 5.Compute the MST for the sparsified graph 19

20. Compute-in-parallel and merge 20

21. Personalized PageRank Like the global PageRank: But the random walker that comes back to where it started with probability d For every v you will have a personalized page rank vector of length N. We usually keep only a limited number of top personalized PageRanks for each node. It finds the influential nodes in the proximity of a given node. 21

22. Monte Carlo Approximation Simulate many random walks from every single node. For each walk: 1.A walk starting from node v is identified by v Keep track of where U v,t is the current end point at step t for the walk starting at node v 2.In each Map-Reduce step advance the walk by 1 step Pick a random neighbor of U v,t 3.Count the frequency of visits to each node 22

23. One can do better [Das Sarma 08] This takes T steps for a walk of length T We can cut it down to T 1/2 by a simple stitching idea 1.Do T/J random walks from every node for some J 2.To for a walk of length T, pick one of the T/J segments at random and jump to the end of the segment 3.Pick another random segment, etc 4.If you arrive at a node twice, do not use the same segment (thats why you need T/J segments) Total iterations: J+T/J minimized when J=T 1/2 O(T 1/2 ) 23

24. Exponential speed up [Bahmani 11] The stitching was done somewhat serially (at each step, one segment was stitched to another) Idea: Stich recursively, which will result in exponentially expanding the walk/segment ratio Takes a little more tricks to make it work, but you can bring it down to O(log T) 24

25. Labeling Connected Components Assign the same ID to all nodes inside the same component 25 12 3 4 5 6

26. How do we do it on one machine? 1.i=1 2.Pick a random node you have not picked before, assign it id=i and put it in a stack 3.Pop a node from the stack, pull all its neighbors we have not seen before into the stack. Assign them id=i 4.If stack is not empty go to 3, otherwise i i+1 and go to 2 Time and memory complexity O(M). 26 12 3 4 5 6

27. In Map-Reduce: More Parallelizim Instead of growing a frontier zone from a single seed, start growing it from all nodes. When two zones meet, merge them 27 14 3 2 Edge File Zone File

28. Game Plan 28 New Zone File Bin Zone and Edge by Node Bin edge to zone map Collect over edges A zone to zone map Reconcile zones Reassign zones to nodes 14 3 2

29. Analysis Communication: O(M+N) Number of rounds: O(d) where d is the diameter of the graph. Most real graphs have small diameters. Random graph: d=O(log N) This works worst for a path-graph An algorithm with O(M+N) communication and O(log n) round exists for all graphs [Rastogi 12] Uses an idea similar to MinHash 29

30. Intuits GraphEdge A (hopefully soon to be open sourced) graph processing package for Hadoop built on Cascading Efficient support of many core graph processing algorithms: State of the art algorithms Industry-grade test for scalability Will take a few more months to release. Would love to gauge your interest 30

31. Intuits Commercial Graph Think of a graph in which a node is a business, or a consumer An edge is a transaction between these entities The entities are either direct clients of Intuits many offerings, or are business partners of Intuits clients We experiment with a toy version of this graph: about 200M nodes and 10B edges. 31

32. References Cohen, Jonathan. "Graph twiddling in a MapReduce world." Computing in Science & Engineering 11.4 (2009): 29-41. Suri, Siddharth, and Sergei Vassilvitskii. "Counting triangles and the curse of the last reducer." Proceedings of the 20th international conference on World wide web. ACM, 2011. Bahmani Bahman, Kaushik Chakrabarti, and Dong Xin. "Fast personalized pagerank on mapreduce." Proceedings of the 37th SIGMOD international conference on Management of data. 2011. A. Das Sarma, S. Gollapudi, and R. Panigrahy. Estimating PageRank on graph streams. In PODS, pages 69–78, 2008. Foto N. Afrati, Dimitris Fotakis, Jeffrey D. Ullman, Enumerating Subgraph Instances Using Map-Reduce. http://arxiv.org/abs/1208.0615 2012 http://arxiv.org/abs/1208.0615 Lattanzi, Silvio, et al. "Filtering: a method for solving graph problems in mapreduce. 2011. 32