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Moving Social Network Mining Algorithms to the MapReduce World August 23, 2012 KAIST Jae-Gil Lee 강연자 소개 - 이재길 교수 약력 2010년 12월~현재: KAIST 지식서비스공학과 조교수 2008년 9월~2010년 11월: IBM Almaden Research Center 연구원 2006년 7월~2008년 8월: University of Illinois at UrbanaChampaign 박사후연구원 연구분야 시공간 데이터 마이닝 (경로 및 교통 데이터) 소셜 네트워크 및 그래프 데이터 마이닝 빅 데이터 분석 (MapReduce 및 Hadoop) 연락처 E-mail: 홈페이지: http://dm.kaist.ac.kr/jaegil 2012-08-23 2 KAIST 지식서비스공학과 지식서비스공학은 인지공학, 인공지능, IT 기술, 의사결정, HCI, 빅데이터 분석 등의 지식 관련 기술을 융합하여 인간과 IT 시스템과의 소통과 협력을 혁신하는 지능적 지식서비스를 연구 개발하는 것을 목표로 하 고 있으며 지식서비스 발전의 중심축을 이루는 학문이다. 홈페이지: http://kse.kaist.ac.kr/ 2012-08-23 3 Contents 1 Big Data and Social Networks 2 MapReduce and Hadoop 3 Data Mining with MapReduce 4 Social Network Data Mining with MapReduce 5 Conclusions 1. Big Data and Social Networks Big Data (1/2) Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze [IDC] IDC forecast of the size of “digital universe” in 2011 is 1.8 zettabytes (A zettabyte is one billion terabytes!) The size of datasets that qualify as big data will also increase The three V’s: Volume, Velocity, Variety Velocity refers to the low-latency, real-time speed at which analytics needs to be applied Volume refers to "internet scale" Variety means that data is in all sorts of forms all over the place 2012-08-23 6 Big Data (2/2) 2012-08-23 7 Big Data Social Networks The online social network (OSN) is one of the main sources of big data 2012-08-23 8 Data Growth in Facebook 2012-08-23 9 Data Growth in Twitter 2012-08-23 10 Some Statistics on OSNs Twitter is estimated to have 140 million users, generating 340 million tweets a day and handling over 1.6 billion search queries per day As of May 2012, Facebook has more than 900 million active users; Facebook has 138.9 million monthly unique U.S. visitors in May 2011 2012-08-23 11 Social Network Service An online service, platform, or site that focuses on building and reflecting of social networks or social relations among people, who, for example, share interests and/or activities [wikipedia] Consists of a representation of each user (often a profile), his/her social links, and a variety of additional services Provides service using web or mobile phones 2012-08-23 12 Popular OSN Services Facebook, Twitter, LinkedIn, 싸이월드, 카카오스토리, 미투데이 Inherently created for social networking Flickr, YouTube Originally created for content sharing Also allow an extensive level of social interaction • e.g., subscription features Foursquare, Google Latitude, 아임IN Geosocial networking services 2012-08-23 13 Other Forms of OSNs MSN Messenger, Skype, Google Talk, 카카오톡 Can be considered as an indirect form of social networks Bibliographic networks (e.g., DBLP, Google Scholar) Co-authorship data Citation data Blogs (e.g., Naver Blog) Neighbor list 2012-08-23 14 Data Characteristics Relationship data: e.g., follower, … Content data: e.g., tweets, … Location data contents a user location 2012-08-23 relationship 15 Graph Data A social network is usually modeled as a graph A node → an actor An edge → a relationship or an interaction 2012-08-23 16 Directed or Undirected? Edges can be either directed or undirected Undirected edge (or symmetric relationship) No direction in edges Facebook friendship: if A is a friend of B, then B B should be also a friend of A A Directed edge (or asymmetric relationship) Direction does matter in edges Twitter following: although A is a follower of B, B may not be a follower of A A B 2012-08-23 17 Weight on Edges? Edges can be weighted A B Examples of weight? Geographical social networking data → ? DBLP co-authorship data → ? … 2012-08-23 18 Two Types of Graph Data Multiple graphs (each of which may possibly be of modest size) e.g., chemical compound database 2012-08-23 A single large graph Scope of this tutorial e.g., social network data (in the previous page) 19 2. MapReduce and Hadoop Note: Some of the slides in this section are from KDD 2011 tutorial “Large-scale Data Mining: MapReduce and Beyond” Big Data Analysis To handle big data, Google proposed a new approach called MapReduce MapReduce can crunch huge amounts of data by splitting the task over multiple computers that can operate in parallel No matter how large the problem is, you can always increase the number of processors (that today are relatively cheap) 2012-08-23 21 MapReduce Basics Map step: The master node takes the input, divides it into smaller subproblems, and distributes them to worker nodes. The worker node processes the smaller problem, and passes the answer back to its master node. Example: Reduce step: The master node then collects the answers to all the subproblems and combines them in some way to form the output – the answer to the problem it was originally trying to solve. 2012-08-23 22 Example – Programming Model mapper employees.txt # LAST Smith Brown Johnson Yates Miller Moore Taylor Smith Harris ... ... FIRST John David George John Bill Jack Fred David John ... ... SALARY $90,000 $70,000 $95,000 $80,000 $65,000 $85,000 $75,000 $80,000 $90,000 ... ... Q: “What is the frequency of each first name?” 2012-08-23 def getName (line): return line.split(‘\t’)[1] reducer def addCounts (hist, name): hist[name] = \ hist.get(name,default=0) + 1 return hist input = open(‘employees.txt’, ‘r’) intermediate = map(getName, input) result = reduce(addCounts, \ intermediate, {}) 23 Example – Programming Model mapper employees.txt # LAST Smith Brown Johnson Yates Miller Moore Taylor Smith Harris ... ... FIRST John David George John Bill Jack Fred David John ... ... SALARY $90,000 $70,000 $95,000 $80,000 $65,000 $85,000 $75,000 $80,000 $90,000 ... ... Q: “What is the frequency of each first name?” 2012-08-23 def getName (line): return (line.split(‘\t’)[1], 1) reducer def addCounts (hist, (name, c)): hist[name] = \ hist.get(name,default=0) + c return hist input = open(‘employees.txt’, ‘r’) intermediate = map(getName, input) result = reduce(addCounts, \ intermediate, {}) Key-value iterators 24 Example – Programming Model Hadoop / Java public class HistogramJob extends Configured implements Tool { public static class FieldMapper extends MapReduceBase implements Mapper<LongWritable,Text,Text,LongWritable> { typed… private static LongWritable ONE = new LongWritable(1); private static Text firstname = new Text(); @Override public void map (LongWritable key, Text value, OutputCollector<Text,LongWritable> out, Reporter r) { firstname.set(value.toString().split(“\t”)[1]); output.collect(firstname, ONE); non-boilerplate } } // class FieldMapper 2012-08-23 25 Example – Programming Model Hadoop / Java public static class LongSumReducer extends MapReduceBase implements Mapper<LongWritable,Text,Text,LongWritable> { private static LongWritable sum = new LongWritable(); @Override public void reduce (Text key, Iterator<LongWritable> vals, OutputCollector<Text,LongWritable> out, Reporter r) { long s = 0; while (vals.hasNext()) s += vals.next().get(); sum.set(s); output.collect(key, sum); } } // class LongSumReducer 2012-08-23 26 Example – Programming Model Hadoop / Java public int run (String[] args) throws Exception { JobConf job = new JobConf(getConf(), HistogramJob.class); job.setJobName(“Histogram”); FileInputFormat.setInputPaths(job, args[0]); job.setMapperClass(FieldMapper.class); job.setCombinerClass(LongSumReducer.class); job.setReducerClass(LongSumReducer.class); // ... JobClient.runJob(job); return 0; } // run() public static main (String[] args) throws Exception { ToolRunner.run(new Configuration(), new HistogramJob(), args); } // main() } // class HistogramJob 2012-08-23 27 Execution Model: Flow Key/value iterators Input file Smith John $90,000 SPLIT 0 John 1 MAPPER John John SPLIT 2 Output file REDUCER SPLIT 1 Yates 2 MAPPER $80,000 John 1 REDUCER MAPPER PART 0 PART 1 SPLIT 3 MAPPER Sort-merge All-to-all, hash partitioning 2012-08-23 Sequential scan 28 Execution Model: Placement HOST 1 SPLIT 0 Replica 2/3 HOST 2 SPLIT 4 Replica 1/3 MAPPER SPLIT 3 HOST 0 SPLIT 0 Replica 1/3 SPLIT 3 Replica 3/3 SPLIT 2 Replica 2/3 MAPPER HOST 3 SPLIT 0 Replica 1/3 Replica 3/3 SPLIT 1 SPLIT 2 Replica 3/3 SPLIT 1 Replica 1/3 Replica 2/3 MAPPER MAPPER SPLIT 3 Computation co-located with data (as much as possible) SPLIT 4 Replica 2/3 Replica 2/3 HOST 5 HOST 6 2012-08-23 HOST 4 29 Execution Model: Placement HOST 1 SPLIT 0 Replica 2/3 HOST 2 SPLIT 4 Replica 1/3 MAPPER C SPLIT 3 HOST 0 SPLIT 0 Replica 1/3 Replica 1/3 SPLIT 3 Replica 3/3 SPLIT 2 Replica 2/3 MAPPER HOST 3 C SPLIT 0 REDUCER Replica 3/3 SPLIT 1 SPLIT 2 Replica 3/3 SPLIT 1 Replica 1/3 Replica 2/3 MAPPER C MAPPER C SPLIT 3 SPLIT 4 Rack/network-aware Replica 2/3 Replica 2/3 HOST 5 HOST 6 C HOST 4 COMBINER 2012-08-23 30 Apache Hadoop The most popular open-source implementation of MapReduce http://hadoop.apache.org/ HBase Pig MapReduce Core 2012-08-23 Hive Chukwa HDFS Zoo Keeper Avro 31 Apache Mahout (1/2) A scalable machine learning and data mining library built upon Hadoop Currently, ver 0.7: the implementation details are not known http://mahout.apache.org/ Supporting algorithms Collaborative filtering User and Item based recommenders K-Means, Fuzzy K-Means clustering Singular value decomposition Parallel frequent pattern mining Complementary Naive Bayes classifier Random forest decision tree based classifier … 2012-08-23 32 Apache Mahout (2/2) Data structure for vectors and matrices Vectors • Dense vectors as a double[] • Sparse vectors as a HashMap<Integer, Double> • Operations: assign, cardinality, copy, divide, dot, get, haveSharedCells, like, minus, normalize, plus, set, size, times, toArray, viewPart, zSum, and cross Matrices • Dense matrix as a double[][] • SparseRowMatrix or SparseColumnMatrix as a Vector[] as holding the rows or columns of the matrix in a SparseVector • SparseMatrix as a HashMap<Integer, Vector> • Operations: assign, assignColumn, assignRow, cardinality, copy, divide, get, haveSharedCells, like, minus, plus, set, size, times, transpose, toArray, viewPart, and zSum 2012-08-23 33 3. Data Mining with MapReduce Clustering Basics Grouping data to form new categories (clusters) Principle: maximizing intra-cluster similarity and minimizing inter-cluster similarity e.g., customer locations in a city two clusters 2012-08-23 35 k-Means Clustering (1/3) 1. Arbitrarily choose k points from D as the initial cluster centers 2. (Re)assign each point to the cluster to which the point is the most similar, based on the mean value of the points in the cluster (centroid) 3. Update the cluster centroids, i.e., calculate the mean value of the points for each cluster 4. Repeat 2~3 until the criterion function converges 2012-08-23 36 k-Means Clustering (2/3) Iteration 6 1 2 3 4 5 3 2.5 2 y 1.5 1 0.5 0 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x 2012-08-23 37 k-Means Clustering (3/3) Iteration 1 Iteration 2 Iteration 3 2.5 2.5 2.5 2 2 2 1.5 1.5 1.5 y 3 y 3 y 3 1 1 1 0.5 0.5 0.5 0 0 0 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 -2 -1.5 -1 -0.5 x 0 0.5 1 1.5 2 -2 Iteration 4 Iteration 5 2.5 2 2 2 1.5 1.5 1.5 1 1 1 0.5 0.5 0.5 0 0 0 -0.5 0 x 2012-08-23 0.5 1 1.5 2 0 0.5 1 1.5 2 1 1.5 2 y 2.5 y 2.5 y 3 -1 -0.5 Iteration 6 3 -1.5 -1 x 3 -2 -1.5 x -2 -1.5 -1 -0.5 0 x 0.5 1 1.5 2 -2 -1.5 -1 -0.5 0 0.5 x 38 k-Means on MapReduce (1/2) [Chu et al., 2006] Map: assigning each point to the closet centroid Mapper1 Map (point p, the set of centroids): for c in centroids do if dist(p, c) < minDist then minDist = dist(p, c) closestCentroid = c emit (closestCentroid, p) Data Mapper2 Map Reduce (1, …), (2, …) 2012-08-23 Split1 Mapper1 Split2 Mapper2 Reducer1 (3, …), (4, …) Reducer2 39 k-Means on MapReduce (2/2) [Chu et al., 2006] Reduce: updating each centroid with newly assigned points Reducer1 Reduce (centroid c, the set of points): for p in points do coordinates += p count += 1 emit (c, coordinates / count) Reducer2 Repeat Data Map Reduce (1, …), (2, …) 2012-08-23 Split1 Mapper1 Reducer1 New centroids for C1 and C2 Split2 Mapper2 Reducer2 New centroids for C3 and C4 (3, …), (4, …) 40 Classification Basics Classifier Unseen data (Jeff, Professor, 4, ?) Features Prediction Feature Generation Training data NAME Mike Mary Bill Jim Dave Anne RANK Assistant Prof Assistant Prof Professor Associate Prof Assistant Prof Associate Prof YEARS 3 7 2 7 6 3 TENURED no yes yes yes no no Tenured = Yes Class label 2012-08-23 41 k-NN Classification (1/2) Intuition behind k-NN classification Compute Distance Training Records 2012-08-23 Test Record Choose the k “nearest” records 42 k-NN Classification (2/2) Compute the distance to other training records Identify k nearest neighbors Use the class labels of the NNs to determine the class label of an unknown record (e.g., by the majority vote) 2012-08-23 Unknown record 43 k-NN Classification on MapReduce (1/2) Map: finding candidates for k-nearest neighbors Obtaining local k-nearest neighbors in the split k=3 Map (query q, the set of points): knns = find k-nearest neighbors from the given set of points // Output the k-NNs in the split emit (q, knns) Data Split1 Split2 2012-08-23 Map Mapper1 Mapper1 Mapper2 Query Reduce local k-nearest neighbors Reducer1 Mapper2 44 k-NN Classification on MapReduce (2/2) Reduce: finding true k-nearest neighbors Obtaining global k-nearest neighbors k=3 Reduce (query q, local neighbors): knns = find k-nearest neighbors among all local neighbors emit (q, knns) Reducer1 Query Only Once Data Split1 Split2 2012-08-23 Map Mapper1 Reduce local k-nearest neighbors Reducer1 k-nearest neighbors Mapper2 45 Naïve Bayes Classifiers (1/2) The probability model for a classifier is a conditional model (|1 , … , ) Using Bayes’ theorem, we write (1 , … , |) 1 , … , = (1 , … , ) Under the conditional independence assumptions, the conditional distribution can be expressed as below 1 1 , … , = () ( |) =1 2012-08-23 46 Naïve Bayes Classifiers (2/2) Example X = (age <=30, income = medium, student = yes, credit_rating = fair) P(buy = “yes”) = 9/14 = 0.643 P(buy = “no”) = 5/14= 0.357 P(age = “<=30” | buy = “yes”) = 2/9 = 0.222 P(age = “<= 30” | buy = “no”) = 3/5 = 0.6 P(income = “medium” | buy = “yes”) = 4/9 = 0.444 P(income = “medium” | buy = “no”) = 2/5 = 0.4 P(student = “yes” | buy = “yes) = 6/9 = 0.667 P(student = “yes” | buy = “no”) = 1/5 = 0.2 P(credit_rating = “fair” | buy = “yes”) = 6/9 = 0.667 P(credit_rating = “fair” | buy = “no”) = 2/5 = 0.4 P(X | buy = “yes”) = 0.222 x 0.444 x 0.667 x 0.667 = 0.044 P(X | buy = “no”) = 0.6 x 0.4 x 0.2 x 0.4 = 0.019 0.643 x 0.044 = 0.028 vs. 0.357 x 0.019 = 0.007 buy = yes 2012-08-23 age income student credit_rating buy <=30 high no fair no <=30 high no excellent no 31…40 high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no 31…40 low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes 31…40 medium no excellent yes 31…40 high yes fair yes >40 medium no excellent no Training Data 47 Naïve Bayes Classifiers on MapReduce (1/3) [Chu et al., 2006] We have to estimate ( = | = 1), ( = | = 0), () from the training data For simplicity, we consider two-class problems Thus, we count the number of records in parallel 1. 1{ = | = 1} 2. 1{ 3. 1{ = 1} 4. 1{ = 0} 2012-08-23 = | = 0} 48 Naïve Bayes Classifiers on MapReduce (2/3) [Chu et al., 2006] Map: counting the number of records for 1~4 in the previous page (1) For conditional probability (2) For class distribution Map (record): emit ((Aj, C), 1) Data Map (record): emit (C, 1) Map Reduce Counts for C=1 2012-08-23 Split1 Mapper1 Split2 Mapper2 Reducer1 Counts for C=0 Reducer2 49 Naïve Bayes Classifiers on MapReduce (3/3) [Chu et al., 2006] Reduce: summing up the counts (1) For conditional probability (2) For class distribution Reduce ((Aj, C), counts): total += counts emit ((Aj, C), total) Reduce (C, counts): total += counts emit (C, total) Only Once Data Map Reduce Counts for C=1 2012-08-23 Split1 Mapper1 Reducer1 = 1 , ( = 1) Split2 Mapper2 Reducer2 = 0 , ( = 0) Counts for C=0 50 4. Social Network Data Mining with MapReduce Degree Statistics Vertex in-degree: how many incoming edges does a vertex X have? Graph in-degree distribution: how many vertices have X number of incoming edges? These two fundamental statistics serve as a foundation for more quantifying statistics developed in the domains of graph theory and network science 2012-08-23 52 Computing Degree Statistics on MapReduce [Cohen, 2009] 2 input graph 1 3 vertex in-degree key value 1 2 1 2 3 graph in-degree distribution key value key value 1 1 1 1 2 1 2 3 1 3 3 3 1 key value 1 1 2 1 key 1 value 1 2 2 1 1 2 1 Map 2012-08-23 Reduce Map Reduce 53 Clustering Coefficient (1/3) A measure of degree to which nodes in a graph tend to cluster together Definition: Local clustering coefficient cc(v) = # of edges between neighbors of a node v / # of possible edges between neighbors of v {(, ) ∈ | ∈ Γ() ∧ ∈ Γ() = 2 N/A ′ # Δ s incidient on = 2 2012-08-23 1 3 1 1 54 Clustering Coefficient (2/3) Example clustering coefficient( ) = 0.5 clustering coefficient( ) = 0.1 It captures how tight-knit the network around a node Network cohesion: tightly-knit communities foster more trust and social norms 2012-08-23 55 Clustering Coefficient (3/3) Approach Computing the clustering coefficient of each node reduces to computing the number of triangles incident on each node Sequential algorithm for v ∈V do for u, w ∈ Γ() do if (u, w) ∈ E then Triangles[v]++ 2012-08-23 Triangles[v]=1 v w u 56 Counting Triangles on MapReduce (1/4) [Suri et al., 2011] Basic algorithm Map 1: • For each ∈ , send Γ() to a reducer 1 2 3 4 Reduce 1: • Generate all 2-paths of the form <1 , 2 ; >, where 1 , 2 ∈ Γ() Map 2: • Send <1, 2 ; > to a reducer, • Send graph edges <1, 2 ; $> to the reducer 2 Reduce 2: input <1 , 2 ; 1 , …, , $?> • If $ in input, then 1 , 2 get k/3 Δ’s each, and 1 , ..., get 1/3 Δ’s each 2012-08-23 1 2 1 57 Counting Triangles on MapReduce (2/4) [Suri et al., 2011] Example After Map 1 & Reduce 1 ; ; ; ; ; After Map 2 & Reduce 2 2012-08-23 ; ; $ +1/3 +1/3 +1/3 ; ; ; $ +1/3 +1/3 +1/3 $ +1/3 +1/3 +1/3 58 Counting Triangles on MapReduce (3/4) [Suri et al., 2011] Possible improvement Generating 2-paths around high-degree nodes is expensive We can make the lowest-degree node responsible for counting the triangle • Let ≻ be the total order on nodes such that ≻ if > • 2-paths < , ; > are generated only if ≺ and ≺ < , ; > 2012-08-23 59 Counting Triangles on MapReduce (4/4) [Suri et al., 2011] Improved algorithm Map 1: • If ≻ , emit < ; > ≺ Reduce 1: input < ; ⊆ Γ > ≺ ≺ • Generate all 2-paths of the form < 1 , 2 ; >, where 1 , 2 ∈ ; Map 2 and Reduce 2 are the same as before except 1 (instead of 1/3) is added to each node ; 2012-08-23 $ +1 +1 +1 60 Finding Trusses [Cohen 2009] A k-truss is a relaxation of a k-clique and is a nontrivial, single-component maximal subgraph, such that every edge is contained in at least k - 2 triangles in the subgraph (a) 3-trusses 2012-08-23 (b) 4-trusses (c) 5-trusses 61 Finding Trusses on MapReduce [Cohen 2009] Input: triangles Map Output: # of triangles for each edge Reduce k=4 Output: pass edges that occur in a sufficient number of triangles 2012-08-23 62 PageRank Overview (1/4) Google describes PageRank: “… PageRank also considers the importance of each page that casts a vote, as votes from some pages are considered to have greater value, thus giving the linked page greater value. … and our technology uses the collective intelligence of the web to determine a page's importance” A page referenced by many high-quality pages is also a high-quality page 2012-08-23 63 PageRank Overview (2/4) Formula OR PR(A): PageRank of a page A d: the probability, at any step, that the person will continue which is called a damping factor d (usually, set to be 0.85) L(B): the number of outbound links on a page B N: the total number of pages 2012-08-23 64 PageRank Overview (3/4) Example PR(A) = (1–d) * (1/N) + d * (PR(C) / 2) PR(B) = (1–d) * (1/N) + d * (PR(A) / 1 + PR(C) / 2) PR(C) = (1–d) * (1/N) + d * (PR(B) / 1) Set d = 0.70 for ease of calculation PR(A) = 0.1 + 0.35 * PR(C) PR(B) = 0.1 + 0.70 * PR(A) + 0.35 * PR(C) B PR(C) = 0.1 + 0.70 * PR(B) Iteration 1: PR(A) = 0.33, PR(B) = 0.33, PR(C) = 0.33 Iteration 2: PR(A) = 0.22, PR(B) = 0.45, PR(C) = 0.33 Iteration 3: PR(A) = 0.22, PR(B) = 0.37, PR(C) = 0.41 … Iteration 9: PR(A) = 0.23, PR(B) = 0.39, PR(C) = 0.38 2012-08-23 A C 65 PageRank Overview (4/4) A random surfer selects a page and keeps clicking links until getting bored, then randomly selects another page PR(A) is the probability that such a user visits A (1-d) is the probability of getting bored at a page (d is called the damping factor) PageRank matrix can be computed offline Google takes into account both the relevance of the page and PageRank 2012-08-23 66 PageRank on MapReduce (1/2) [Lin et al., 2010] Map: distributing PageRank “credit” to link targets Reduce: summing up PageRank “credit” from multiple sources to compute new PageRank values Iterate until convergence 2012-08-23 67 PageRank on MapReduce (2/2) [Lin et al., 2010] Map (nid n, node N) p ← N.PageRank / |N.AdjacencyList| emit (nid n, node N) // Pass along the graph structure for nid m ∈ N.AdjacencyList do emit (nid m, p) // Pass a PageRank value to its neighbors Reduce (nid m, [p1, p2, …]) M←0 for p ∈ [p1, p2, …] do if IsNode(p) then M←p // Recover the graph structure else s←s+p // Sum up the incoming PageRank contributions M.PageRank ← s emit (nid m, node M) 2012-08-23 68 Pegasus Graph Mining System [Kang et al., 2009] GIM-V Generalized Iterative Matrix-Vector Multiplication Extension of plain matrix-vector multiplication Including the following algorithms as special cases • • • • 2012-08-23 Connected Components PageRank Random Walk with Restart (RWR) Diameter Estimation 69 Data Model A matrix represents a graph Each column or row represents a node , represents the weight of the edge from i to j Example: column-normalized adjacency matrix 1 1 1/2 2 1 1 3 1/2 1/2 1/2 4 5 A vector represents some value of nodes, e.g., PageRank 2012-08-23 70 Main Idea of GIM-V (1/2) The matrix-vector multiplication is × = ′ where ′ = =1 , M v’ v X = There are three operations in the above formula combine2: multiply , and combineAll: sum multiplication results for a node i assign: overwrite the previous value of with a new result to make ′ 2012-08-23 71 Main Idea of GIM-V (2/2) The operator × is defined as follows: ′ = × , where ′ = assign( , combineAlli({ | =1, …, and = combine2(, , )})) combine2(, , ): combine , and combineAlli(1 , … , ): combine all the results from combine2() for a node i assign( , ): decide how to update with × is applied until a convergence criterion is met Customizing the three functions implements several graph mining operations e.g., PageRank, Random Walk with Restart, … 2012-08-23 72 Connected Components How many connected components? Which node belongs to which component? component id 1 5 7 2 3 6 8 4 A 2012-08-23 B C 1 A 1 1 2 A 2 1 3 A 3 1 4 A 4 1 5 B 5 5 6 B 6 5 7 C 7 7 8 C 8 7 or 73 GIM-V and Connected Components We need to design a proper matrix vector multiplication 1 1 1 5 7 2 2 3 3 6 8 6 B C 4 5 6 7 8 1 1 1 1 1 1 1 1 1 2 1 3 1 4 1 5 1 7 8 2012-08-23 3 5 4 A 4 2 1 1 ? 5 6 5 7 7 8 7 initial vector final vector 74 Naïve Method (GIM-V BASE) (1/2) 1 1 2 3 2 3 4 5 6 7 8 1 1 1 1 4 1 1 5 6 1 1 7 8 2012-08-23 1 1 1 min(1, min(2) ) 1 2 min(2, min(1,3) ) 1 3 min(3, min(2,4) ) 2 4 min(4, min(3) ) 3 5 min(5, min(6) ) 5 6 min(6, min(5) ) 5 7 min(7, min(8) ) 7 8 min(8, min(7) ) 7 75 Naïve Method (GIM-V BASE) (2/2) 1 1 2 3 2 3 4 5 6 7 8 1 1 1 1 4 1 1 5 6 1 1 7 8 2012-08-23 1 1 1 min(1, min(2) ) 1 1 1 2 min(2, min(1,3) ) 1 1 1 3 min(3, min(2,4) ) 2 1 1 4 min(4, min(3) ) 3 2 1 5 min(5, min(6) ) 5 5 5 6 min(6, min(5) ) 5 5 5 7 min(7, min(8) ) 7 7 7 8 min(8, min(7) ) 7 7 7 76 Implementation of GIM-V BASE 1 1 2 Input: Matrix(src, dst) Vector(id, val) 3 4 2 3 4 5 7 1 8 dst 1 1 1 2 1 3 1 4 1 5 1 6 7 src 8 1 5 6 6 1 1 7 8 Stage 1: combine2() Map → Join M and V using M.dst and V.id Reduce→ Output (M.src, V.val) Stage 2: combineAll(), assign() Map → Aggregate (M.src, V.val) by M.src Reduce→ Output (M.src, min(V.val1, V.val2, …)) 2012-08-23 77 GIM-V and PageRank The matrix notation of PageRank p = d B p + (1 - d)/n 1 p = {d B + (1 - d)/n E } p, where E is a matrix of all 1’s M: column-normalized v: PageRank vector adjacency matrix damping factor GIM-V operations for PageRank combine2(, , ) = × , × combineAlli(1 , … , ) = (1−) + =1 assign( , ) = 2012-08-23 78 Pregel [Malewicz et al., 2010] A large-scale distributed framework for graph data developed by Google C++ API: developed and used only internally Phoebus: open-source implementation of Pregel Based on the Erlang programming language https://github.com/xslogic/phoebus 2012-08-23 79 Bulk Synchronous Parallel Model Computations consist of a sequence of iterations, called supersteps Within each superstep, the vertices execute a user-defined function in parallel The function expresses the logic of an algorithm―the behavior at a single vertex and a single superstep It can read messages sent to the vertex from the previous superstep, send messages to other vertices to be received in the next superstep, and modify the state of the vertex or that of its outgoing edges Edges are not first-class citizens 2012-08-23 80 Termination Algorithm termination is based on every vertex voting to halt Vertex state machine Vote to halt Active Inactive Message received The algorithm as a whole terminates when all vertices are simultaneously inactive 2012-08-23 81 Supersteps Example: getting the maximum value 3 6 2 1 Superstep 0 6 6 2 6 Superstep 1 6 6 6 6 Superstep 2 6 6 6 6 Superstep 3 Edge 2012-08-23 Message Voted to halt 82 Implementation of Pregel Basic architecture The executable is copied to many machines One machine: Master ← coordinating computation Other machines: Workers ← performing computation Basic stages 1. 2. 3. 4. 2012-08-23 Master partitions the graph Master assigns the input to each Worker Supersteps begin at Workers Master can tell Workers to save graphs 83 C++ API Compute() Executed at each active vertex in every superstep Overridden by a user GetValue() or MutableValue() Inspect or modify the value associated with a vertex GetOutEdgeIterator(): Get the iterator of out-going edges SendMessageTo(): Deliver a message to given vertices VoteToHalt() 2012-08-23 84 Applications of Pregel PageRank implementation in Pregel class PageRankVertex : public Vertex<double, void, double> { public: virtual void Compute(MessageIterator* msgs) { if (superstep() >= 1) { double sum = 0; for (; !msgs->Done(); msgs->Next()) sum += msgs->Value(); *MutableValue() = 0.15 / NumVertices() + 0.85 * sum; } if (superstep() < 30) { const int64 n = GetOutEdgeIterator().size(); SendMessageToAllNeighbors(GetValue() / n); } else { VoteToHalt(); } } }; 2012-08-23 85 Related Research Projects Pegasus (CMU) Overview: [Kang et al., 2009] Belief propagation: [Kang et al., 2011a] Spectral clustering → top k eigensolvers: [Kang et al., 2011b] SystemML (IBM Watson Research Center) Overview: [Ghoting et al., 2011a] • SystemML enables declarative machine learning on Big Data in a MapReduce environment; Machine learning algorithms are expressed in DML, and compiled and executed in a MapReduce environment NIMBLE: [Ghoting et al., 2011b] Cloud9 (University of Maryland) Overview: [Lin et al., 2010a] • Cloud9 is a MapReduce library for Hadoop designed to serve as both a teaching tool and to support research in data-intensive text processing Graph algorithms: [Lin et al., 2010b] 2012-08-23 86 5. Conclusions Conclusions More and more algorithms are moving to the MapReduce world For social networks, most of such algorithms can be represented using matrix manipulation, e.g., PageRank We need to work on developing a parallel version of more-complicated algorithms such as community discovery and influence analysis 2012-08-23 88 References [Chu et al., 2006] Cheng-Tao Chu, Sang Kyun Kim, Yi-An Lin, YuanYuan Yu, Gary R. Bradski, Andrew Y. Ng, Kunle Olukotun: Map-Reduce for Machine Learning on Multicore. NIPS 2006: 281288 [Kang et al., 2009] U. Kang, Charalampos E. Tsourakakis, Christos Faloutsos: PEGASUS: A Peta-Scale Graph Mining System. ICDM 2009: 229-238 [Kang et al., 2011a] U. Kang, Duen Horng Chau, Christos Faloutsos: Mining large graphs: Algorithms, inference, and discoveries. ICDE 2011: 243-254 [Kang et al., 2011b] U. Kang, Brendan Meeder, Christos Faloutsos: Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation. PAKDD (2) 2011: 13-25 [Suri et al., 2011] Siddharth Suri, Sergei Vassilvitskii: Counting triangles and the curse of the last reducer. WWW 2011: 607-614 2012-08-23 89 References (cont’d) [Ene et al., 2011] Alina Ene, Sungjin Im, Benjamin Moseley: Fast clustering using MapReduce. KDD 2011: 681-689 [Morales et al., 2011] Gianmarco De Francisci Morales, Aristides Gionis, Mauro Sozio: Social Content Matching in MapReduce. PVLDB 4(7): 460-469 (2011) [Das et al., 2007] Abhinandan Das, Mayur Datar, Ashutosh Garg, ShyamSundar Rajaram: Google news personalization: scalable online collaborative filtering. WWW 2007: 271-280 [Lin et al., 2010a] Jimmy Lin, Chris Dyer: Data-Intensive Text Processing with MapReduce. Morgan & Claypool Publishers 2010 Cloud9 library: http://lintool.github.com/Cloud9/ [Lin et al., 2010b] Jimmy Lin, Michael Schatz: Design Patterns for Efﬁcient Graph Algorithms in MapReduce. MLG 2010: 78-85 [Cohen, 2009] Jonathan Cohen: Graph Twiddling in a MapReduce World. Computing in Science and Engineering 11(4): 29-41 (2009) 2012-08-23 90 References (cont’d) [Ghoting et al., 2011a] Amol Ghoting, Rajasekar Krishnamurthy, Edwin P. D. Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, Shivakumar Vaithyanathan: SystemML: Declarative machine learning on MapReduce. ICDE 2011: 231-242 [Ghoting et al., 2011b] Amol Ghoting, Prabhanjan Kambadur, Edwin P. D. Pednault, Ramakrishnan Kannan: NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce. KDD 2011: 334-342 [Malewicz et al., 2010] Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, Grzegorz Czajkowski: Pregel: a system for large-scale graph processing. SIGMOD Conference 2010: 135-146 2012-08-23 91 THANK YOU