Mining of Massive Datasets Jure Leskovec, AnandRajaraman, Jeff Ullman Stanford University

Transcription

1 Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. If you make use of a significant portion of these slides in your own lecture, please include this message, or a link to our web site: Mining of Massive Datasets Jure Leskovec, AnandRajaraman, Jeff Ullman Stanford University

2 We often think of networks being organized into modules, cluster, communities: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 2

3 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 3

4 Find micro-markets by partitioning the query-to-advertiser graph: query advertiser [Andersen, Lang: Communities from seed sets, 2006] J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 4

5 Clusters in Movies-to-Actors graph: [Andersen, Lang: Communities from seed sets, 2006] J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 5

6 Discovering social circles, circles of trust: [McAuley, Leskovec: Discovering social circles in ego networks, 2012] J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 6

7 How to find communities? We will work with undirected (unweighted) networks J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 7

8 Edge betweenness:number of shortest paths passing over the edge Intuition: b=16 b=7.5 Edge strengths (call volume) in a real network Edge betweenness in a real network J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 8

9 [Girvan-Newman 02] Divisive hierarchical clustering based on the notion of edge betweenness: Number of shortest paths passing through the edge Girvan-Newman Algorithm: Undirected unweighted networks Repeat until no edges are left: Calculate betweenness of edges Remove edges with highest betweenness Connected components are communities Gives a hierarchical decomposition of the network J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 9

10 Need to re-compute betweenness at every step J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 10

11 Step 1: Step 2: Step 3: Hierarchical network decomposition: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 11

12 Communities in physics collaborations J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 12

13 Zachary s Karate club: Hierarchical decomposition J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 13

14 1. How to compute betweenness? 2. How to select the number of clusters? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 14

15 Want to compute betweennessof paths starting at node Breath first search starting from : J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 15

16 Count the number of shortest paths from to all other nodes of the network: J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 16

17 Compute betweennessby working up the tree:if there are multiple paths count them fractionally The algorithm: Add edge flows: -- node flow = 1+ child edges -- split the flow up based on the parent value Repeat the BFS procedure for each starting node 1 path to K. Split evenly paths to J Split 1:2 1+1 paths to H Split evenly J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 17

18 Compute betweennessby working up the tree:if there are multiple paths count them fractionally The algorithm: Add edge flows: -- node flow = 1+ child edges -- split the flow up based on the parent value Repeat the BFS procedure for each starting node 1 path to K. Split evenly paths to J Split 1:2 1+1 paths to H Split evenly J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 18

19 1. How to compute betweenness? 2. How to select the number of clusters? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 19

20 Communities:sets of tightly connected nodes Define:Modularity A measure of how well a network is partitioned into communities Given a partitioning of the network into groups : Q s S [ (# edges within group s) (expected # edges within group s) ] Need a null model! J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 20

21 Given real on nodes and edges, construct rewired network Same degree distribution but random connections Consider as a multigraph The expected number of edges between nodes and of degrees and equals to: The expected number of edges in (multigraph) G : J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 21 i j Note: 2

22 Modularity of partitioning S of graph G: Q s S [ (# edges within group s) (expected # edges within group s) ], Normalizing cost.: -1<Q<1 A ij = 1 if i j, 0 else Modularity values take range [ 1,1] It is positive if the number of edges within groups exceeds the expected number <Q means significant community structure J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 22

23 Modularity is useful for selecting the number of clusters: Q Next time: Why not optimize Modularity directly? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 23

24

25 Undirected graph, : Bi-partitioning task: Divide vertices into two disjoint groups, A B Questions: How can we define a good partition of? How can we efficiently identify such a partition? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 25

26 What makes a good partition? Maximize the number of within-group connections Minimize the number of between-group connections A B J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 26

27 Express partitioning objectives as a function of the edge cut of the partition Cut:Set of edges with only one vertex in a group: A B cut(a,b) = 2 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 27

28 Criterion: Minimum-cut Minimize weight of connections between groups arg min A,B cut(a,b) Degenerate case: Optimal cut Minimum cut Problem: Only considers external cluster connections Does not consider internal cluster connectivity J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 28

29 [Shi-Malik] Criterion: Normalized-cut [Shi-Malik, 97] Connectivity between groups relative to the density of each group :total weight of the edges with at least one endpoint in : Why use this criterion? Produces more balanced partitions How do we efficiently find a good partition? Problem: Computing optimal cut is NP-hard J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 29

30 A: adjacency matrix of undirected G A ij =1 if, is an edge, else 0 xis a vector in R n with components,, Think of it as a label/value of each node of What is the meaning of A x? Entry y i is a sum of labels x j of neighbors of i J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 30

31 j th coordinate of A x : Sum of the x-values of neighborsof j Make this a new value at node j Spectral Graph Theory: Analyze the spectrum of matrix representing Spectrum:Eigenvectors of a graph, ordered by the magnitude (strength) of their corresponding eigenvalues : J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 31

32 Suppose all nodes in have degree and is connected What are some eigenvalues/vectors of? What is λ? What x? Let s try:,,, Then:,,,. So: We found eigenpairof :,,,, Remember the meaning of : J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 32

33 Details! Gisd-regular connected, Ais its adjacency matrix Claim: dis largest eigenvalue ofa, dhas multiplicity of 1 (there is only 1 eigenvector associated with eigenvalue d) Proof: Why no eigenvalue? To obtain dwe needed for every, This means 1,1,,1 for some const. Define: = nodes with maximum possible value of Then consider some vector which is not a multiple of vector,,. So not all nodes (with labels ) are in Consider some node and a neighbor then node gets a value strictly less than So is not eigenvector! And so is the largest eigenvalue! J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 33

34 What if is not connected? has 2components, each -regular What are some eigenvectors? Put all s on and s on or vice versa,,,,, then,,,,,,,,,, then,,,,, And so in both cases the corresponding A bit of intuition: A A B B A J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 34 B A B 2 nd largest eigval. now has value very close to

35 More intuition: A B A B 2 nd largest eigval. now has value very close to If the graph is connected (right example) then we already know that, is an eigenvector Since eigenvectors are orthogonal then the components of sum to 0. Why? Because So we can look at the eigenvector of the 2 nd largest eigenvalue and declare nodes with positive label in A and negative label in B. But there is still lots to sort out. J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 35

36 Adjacency matrix (A): n nmatrix A=[a ij ], a ij =1if edge between node iand j Important properties: Symmetric matrix Eigenvectors are real and orthogonal J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 36

37 Degree matrix (D): n n diagonal matrix D=[d ii ], d ii =degree of node i J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 37

38 Laplacian matrix (L): n n symmetric matrix What is trivial eigenpair? 4 5,, then and so Important properties: Eigenvalues are non-negative real numbers Eigenvectors are real and orthogonal J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 38

39 Details! (a)all eigenvalues are 0 (b) 0 for every (c) That is, is positive semi-definite Proof: (c) (b): 0 As it is just the square of length of (b) (a): Let be an eigenvalue of. Then by (b) 0so (a) (c): is also easy! Do it yourself. J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 39

40 Fact: For symmetric matrix M: What is the meaning of min x T L x on G? x L x,,, 2 λ = min 2 x x T x M x T x, 2, Node has degree. So, value needs to be summed up times. But each edge, has two endpoints so we need J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 40

41 λ = min 2 x T x M x T x x Details! Write in axes of eigenvecotrs,,, of. So, Then we get: So, what is? To minimize this over all unit vectors x orthogonal to: w = min over choices of, so that: 1 (unit length) 0(orthogonal to ) To minimize this, set and so if 1 otherwise J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 41

42 What else do we know about x? is unit vector: is orthogonal to 1 st eigenvector,, thus: Remember: λ 2 = min All labelings of nodes so that 0 ( i, j) E ( x i i 2 i We want to assign values to nodes i such that few edges cross 0. (we want x i and x j to subtract each other) x x j ) 2 0 Balance to minimize x J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 42

43 Back to finding the optimal cut Express partition (A,B) as a vector We can minimize the cut of the partition by finding a non-trivial vector x that minimizes: Can t solve exactly. Let s relax and allow it to take any real value J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 43

44 0 x :The minimum value of is given by the 2 nd smallest eigenvalue λ 2 of the Laplacian matrix L :The optimal solution for y is given by the corresponding eigenvector, referred as the Fiedler vector J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 44

45 Details! Suppose there is a partition of Ginto Aand B # where, s.t. then 2 This is the approximation guarantee of the spectral clustering. It says the cut spectral finds is at most 2 away from the optimal one of score. Proof: Let: a= A, b= B and e=# edges from Ato B Enough to choose some based on AandBsuch that: is only smaller 2 (while also 0 ) J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 45

46 Details! Proof (continued): 1)Let s set: Let s quickly verify that 0: 2) Then:, e number of edges between A and B J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 46 Which proves that the cost achieved by spectral is better than twice the OPT cost

47 Details! Putting it all together: where is the maximum node degree in the graph Note we only provide the 1 st part: We did not prove Overall this always certifies that always gives a useful bound J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 47

48 How to define a good partition of a graph? Minimize a given graph cut criterion How to efficiently identify such a partition? Approximate using information provided by the eigenvalues and eigenvectors of a graph Spectral Clustering J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 48

49 Three basic stages: 1) Pre-processing Construct a matrix representation of the graph 2) Decomposition Compute eigenvalues and eigenvectors of the matrix Map each point to a lower-dimensional representation based on one or more eigenvectors 3) Grouping Assign points to two or more clusters, based on the new representation J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 49

50 1)Pre-processing: Build Laplacian matrix Lof the graph 2) Decomposition: Find eigenvalues λ and eigenvectors x of the matrix L Map vertices to corresponding components of λ λ= X = How do we now find the clusters? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 50

51 3)Grouping: Sort components of reduced 1-dimensional vector Identify clusters by splitting the sorted vector in two How to choose a splitting point? Naïve approaches: Split at 0or median value More expensive approaches: Attempt to minimize normalized cut in 1-dimension (sweep over ordering of nodes induced by the eigenvector) Split at 0: Cluster A: Positive points Cluster B: Negative points J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 51 A B

52 Value of x 2 Rank in x 2 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 52

53 Components of x 2 Value of x 2 Rank in x 2 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 53

54 Components of x 1 Components of x 3 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 54

55 How do we partition a graph into kclusters? Two basic approaches: Recursive bi-partitioning[hagen et al., 92] Recursively apply bi-partitioning algorithm in a hierarchical divisive manner Disadvantages: Inefficient, unstable Cluster multiple eigenvectors[shi-malik, 00] Build a reduced space from multiple eigenvectors Commonly used in recent papers A preferable approach J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 55

56 Approximates the optimal cut [Shi-Malik, 00] Can be used to approximate optimal k-way normalized cut Emphasizes cohesive clusters Increases the unevenness in the distribution of the data Associations between similar points are amplified, associations between dissimilar points are attenuated The data begins to approximate a clustering Well-separated space Transforms data to a new embedded space, consisting of k orthogonal basis vectors Multiple eigenvectors prevent instability due to information loss J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 56

57

58 [Kumar et al. 99] Searching for small communities in the Web graph What is the signature of a community / discussion in a Web graph? Use this to define topics : What the same people on the left talk about on the right Remember HITS! Dense 2-layer graph Intuition: Many people all talking about the same things J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 58

59 A more well-defined problem: Enumerate complete bipartite subgraphsk s,t Where K s,t : snodes on the left where each links to the same tother nodes on the right X K 3,4 Y X = s = 3 Y = t = 4 Fully connected J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 59

60 [Agrawal-Srikant 99] Market basket analysis. Setting: Market:Universe Uof nitems Baskets:msubsets of U: S 1, S 2,, S m U (S i is a set of items one person bought) Support: Frequency threshold f Goal: Find all subsets T s.t. T S i of at least f sets S i (items in Twere bought together at least f times) What s the connection between the itemsets and complete bipartite graphs? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 60

61 [Kumar et al. 99] Frequent itemsets = complete bipartite graphs! How? View each node ias a set S i of nodes ipoints to K s,t = a set Yof size t that occurs in ssets S i Looking for K s,t set of frequency threshold to s and look at layer t all frequent sets of size t i X j i k a b c d S i ={a,b,c,d} a b c d Y s minimum support ( X =s) t itemset size ( Y =t) J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 61

62 [Kumar et al. 99] View each node ias a set S i of nodes ipoints to i a b c d S i ={a,b,c,d} Find frequent itemsets: s minimum support t itemset size We found K s,t! K s,t = a set Yof size t that occurs in ssets S i x Say we find a frequent itemset Y={a,b,c} of supp s So, there are snodes that link to all of {a,b,c}: a b c X J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 62 y x y z a b c a b c z Y a b c

63 a c d b e f Itemsets: a = {b,c,d} b = {d} c = {b,d,e,f} d = {e,f} e = {b,d} f = {} Support threshold s=2 {b,d}: support 3 {e,f}: support 2 And we just found 2 bipartite subgraphs: a c e d b J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 63 c e f d

64 Example of a community from a web graph Nodes on the right Nodes on the left [Kumar, Raghavan, Rajagopalan, Tomkins: Trawling the Web for emerging cyber-communities 1999] J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 64