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Graph Mining and Social Network Analysis
Outline Graphs and networks Graph pattern mining [Borgwardt & Yan 2008] Graph classification [Borgwardt & Yan 2008] Graph clustering Graph evolution [Leskovec & Faloutsos 2007] Social network analysis [Leskovec & Faloutsos 2007] Trust-based recommendation [Han and Kamber 2006, sections 9.1 and 9.2] References to research papers at the end of this chapter SFU, CMPT 741, Fall 2009, Martin Ester

SFU, CMPT 741, Fall 2009, Martin Ester
Graphs and Networks Basic Definitions Graph G = (V,E) V: set of vertices / nodes E  V x V: set of edges Adjacency matrix (sociomatrix) alternative representation of a graph Network: used as synonym to graph more application-oriented term SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks Basic Definitions Labeled graph set of lables L f: V  L or f: E  L |L| typically small Attributed graph set of attributes with domains D1, . . ., Dd f: V  D1x x Dd |Di| typically large, can be continuous domain SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks Examples SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks More Definitions Neighbors Degree Clustering coefficient of node v fraction of pairs of neigbors of v that are connected Betweenness of node v number of shortest paths (between any pair of nodes) in G that go through v Betweenness of edge e number of shortest paths in G that go through e SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks More Definitions Shortest path distance between nodes v1 and v2 length of shortest path between v1 and v2 also called minimum geodesic distance Diameter of graph G maximum shortest path distance for any pair of nodes in G Effective diameter of graph G distance at which 90% of all connected pairs of nodes can be reached Mean geodesic distance of graph G average minimum geodesic distance for any pair of nodes in G SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks More Definitions Small-world network network with „small“ mean geodesic distance / effective diameter Microsoft Messenger network SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks More Definitions Scale-free networks networks with a power law degree distribution l typically between 2 and 3 P(k) degree k SFU, CMPT 741, Fall 2009, Martin Ester

Graphs and Networks Data Mining Scenarios One large graph mine dense subgraphs or clusters analyze evolution Many small graphs mine frequent subgraphs Two collections of many small graphs classify graphs SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Frequent Pattern Mining
Given a graph dataset DB, i.e. a set of labeled graphs G1, . . ., Gn and a minimum support Find the graphs that are contained in at least of the graphs of DB Assumption: the more frequent, the more interesting a graph G contained in Gi : G is isomorph to a subgraph of Gi SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Example SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Anti-Monotonicity Property
If a graph is frequent, all of its subgraphs are frequent. Can prune all candidate patterns that have an infrequent subgraph, i.e. disregard them from further consideration. The higher , the more effective the pruning SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Algorithmic Schemes SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Duplicate Elimination
Given existing patterns G1, . . ., Gm and newly discovered pattern G Is G a duplicate? Method 1(slow) check graph isomorphism of G with each of the Gi graph isomorphism test is a very expensive operation Method 2 (faster) transform each graph Gi into a canonical form and hash it into a hash table transform G in the same way and check whether there is already a graph Gi with the same hash value test for graph isomorphism only if such Gi already exists SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Duplicate Elimination Method 3 (fastest)
define a canonical order of subgraphs and explore them in that order e.g., graphs in same equivalence class, if they have the same canonical spanning tree and define order on the spanning trees  does not need isomorhism tests SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Conclusion Lots of sophisticated algorithms for mining frequent graph patterns: MoFa, gSpan, FFSM, Gaston, . . . But: number of frequent patterns is exponential This implies three related problems: - very high runtimes - resulting sets of patterns hard to interpret - minimum support threshold hard to set. SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Research Directions Mine only closed or maximal frequent graphs i.e. frequent graphs so that no supergraph has the same (has at least ) support Summarize graph patterns e.g., find the top k most representative graphs Constraint-based graph pattern mining find only patterns that satisfy certain conditions on their size, density, diameter . . . SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Dense Graph Mining Assumption: the denser a graph, the more interesting Can add density constraint to frequent graph mining In the scenario of one large graph, just want to find the dense subgraphs Density of graph G Want to find all subgraphs with density at least a Problem is notoriously hard, even to solve approximately SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Weak Anti-Monotonicity Property
If a graph of size k is dense, (at least) one of its subgraphs of size k-1 is dense. Cannot prune all candidate patterns that have a subgraph which is not dense. But can still enumerate patterns in a level-wise manner, extending only dense patterns by another node G’ denser than subgraph G density = 8/ density = 14/20 SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Quasi-Cliques graph G is g-quasi-clique if every node has at least SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Mining Quasi-Cliques [Pei, Jiang & Zhang 05]
for g<1, the g-quasi-clique property is not anti-monotone, not even weakly anti-monotone G is 0.8-quasi-clique none of the size 5 subgraphs of G is an 0.8-quasi-clique since they all have a node with degree 3 < 0.8(5-1) = 3.2 SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Mining Quasi-Cliques enumerate (all) the subgraphs prune based on maximum diameter of g-quasi-clique G SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Mining Cohesive Patterns [Moser, Colak and Ester 2009] Cohesive pattern: subgraph G’ satisfying three conditions: (1) subspace homogeneity, i.e. attribute values are within a range of at most w in at least d dimensions, (2) density, i.e. has at least a of all possible edges, and (3) connectedness, i.e. each pair of nodes has a connecting path in G’. Task Find all maximal cohesive patterns. SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Example density = 7/10 = 0.7 = 3 w = 0.0 cohesive patterns density = 8/10 SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Algorithm Cohesive Pattern Mining problem is NP-hard decision version reduceable from Max-Clique problem A constraint is anti-monotone: if for each network G of size n that satisfies the constraint, all induced subnetworks G’ of G of size n - 1 satisfy the constraint Can prune all candidate networks that have a subnetwork not satisfying the constraint  cohesive pattern constraints are not anti-monotone SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Algorithm CoPaM A constraint is loose anti-monotone: if for each network G of size n that satisfies the constraint, there is at least one induced subnetwork G’ of G of size n - 1 satisfying the constraint. For a >= 0.5, the cohesive pattern constraints are loose anti-monotone CoPaM algorithm performs level-wise search of the lattice structure in a bottom-up manner  construct only connected subgraphs Prune all candidates that do not satisfy the constraints of density and homogeniety SFU, CMPT 741, Fall 2009, Martin Ester

Graph Pattern Mining Example = 0.8 = 2 w = 0.5 SFU, CMPT 741, Fall 2009, Martin Ester

Graph Classification Introduction given two (or more) collections of (labeled) graphs one for each of the relevant classes e.g., collections of program flow graphs to distinguish faulty graphs from correct ones SFU, CMPT 741, Fall 2009, Martin Ester

Graph Classification Feature-based Graph Classification
define set of graph features global features such as diameter, degree distribution local features such as occurence of certain subgraphs choice of relevant subgraphs based on domain knowledge domain expert based on frequency pattern mining algorithm [Huan et al 04] SFU, CMPT 741, Fall 2009, Martin Ester

Graph Classification Kernel-based Graph Classification
kernel-based map two graphs x and x‘ into feature space via function compute similarity (inner product) in feature space kernel k avoids actual mapping to feature space many graph kernels have been proposed e.g. [Kashima et al 2003] graph kernels should capture relevant graph features and be efficient to compute [Borgwardt & Kriegel 2005] SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Introduction group nodes into clusters such that nodes within a cluster have similar relationships (edges) while nodes in different clusters have dissimilar relationships compared to graph classification: unsupervised compared to graph pattern mining: global patterns, typically every node belongs to exactly one cluster main approaches - hierarchical graph clustering - graph cuts - block models SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Divisive Hierarchical Clustering [Girvan and Newman 2002] for every edge, compute its betweenness remove the edge with the highest betweenness recompute the edge betweenness repeat until no more edge exists or until specified number of clusters produced runtime O(m2n) where m = |E| and n = |V|  produces meaningful communities, but does not scale to large networks SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Example friendship network from Zachary’s karate club
hierarchical clustering (dendrogram)  shapes denote the true community SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Agglomerative Hierarchical Clustering [Newman 2004]
divisive hierarchical algorithm always produces a clustering, whether there is some natural cluster structure or not define the modularity of a partitioning to measure its meaningfulness (deviation from randomness) eij: percentage of edges between partitions i and j modularity Q SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Agglomerative Hierarchical Clustering
start with singleton clusters in each step, perform the merge of two clusters that leads to the largest increase of the modularity terminate when no more merges improve modularity or when specified number of clusters reached need to consider only connected pairs of clusters runtime O((m+n) n) where m = |E| and n = |V|  scales much better than divisive algorithm clustering quality quite comparable SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering college football network, shapes denote conferences (true communities) SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Graph Cuts graph cut is a set of edges whose removal partitions the set of vertices V into two (disconnected) sets S and T cost of a cut is the sum of the weights of the cut edges edge weights can be derived from node attributes, e.g. similarity of attributes (attribute vectors) minimum cut is a cut with minimum cost SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Graph Cuts [Shi & Malik 2000]
minimum cut tends to cut off very small, isolated components normalized cut where assoc(A, V) = sum of weights of all edges in V that touch A SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Graph Cuts minimum normalized cut problem is NP-hard but approximation can be computed by solving generalized eigenvalue problem SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Block Models [Faust &Wasserman 1992]
actors in a social network are structurally equivalent if they have identical relational ties to and from all the actors in a network partition V into subsets of nodes that have the same relationships i.e., edges to the same subset of V graph represented as sociomatrix partitions are called blocks SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Example graph (sociomatrix) block model
(permuted and partitioned sociomatrix) SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Algorithms agglomerative hierarchical clustering CONCOR algorithm repeated calculations of correlations between rows (or columns) will eventually result in a correlation matrix consisting of only +1and -1 - calculate correlation matrix C1 from sociomatrix - calculate correlation matrix C2 from C1 - iterate until the entries are either +1 or -1 SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Stochastic Block Models
requirement of structural equivalence often too strict relax to stochastic equivalence: two actors are stochastically equivalent if the actors are “exchangeable” with respect to the probability distribution Infinite Relational Model [Kemp et al 2006] SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Generative Model assign nodes to clusters
determine link (edge) probability between clusters determine edges between nodes CMPT 884, SFU, Martin Ester, 1-09

Graph Clustering Generative Model assumption edges conditionally independent given cluster assignments prior P(z) assigns a probability to all possible partitions of the nodes find z that maximizes P(z|R) SFU, CMPT 741, Fall 2009, Martin Ester

Graph Clustering Inference sample from the posterior P(z|R) using Markov Chain Monte Carlo possible moves: - move a node from one cluster to another - split a cluster - merge two clusters at the end, can be recovered SFU, CMPT 741, Fall 2009, Martin Ester

Graph Evolution Introduction so far, have considered only the static structure of networks but many real life networks are very dynamic and evolve rapidly in the course of time two aspects of graph evolution - evolution of the structure (edges): generative models - evolution of the attributes: diffusion models questions, e.g. does the graph diameter increase or decrease? how does information about a new product spread? what nodes should be targeted for viral marketing? SFU, CMPT 741, Fall 2009, Martin Ester

Graph Evolution Generative Models Erdos Renyi model - connect each pair of nodes i.i.d. with probability p  lots of theory, but does not produce power law degree distribution Preferential attachment model - add a new node, create m out-links to existing nodes - probability of linking an existing node is proportional to its degree  produces power law in-degree distribution but all nodes have the same out-degree SFU, CMPT 741, Fall 2009, Martin Ester

Graph Evolution Generative Models Copy model - add a node and choose k, the number of edges to add - with probability β select k random vertices and link to them - with probability 1- β edges are copied from a randomly chosen node  generates power law degree distributions with exponent 1/(1-β) generates communities SFU, CMPT 741, Fall 2009, Martin Ester

Graph Evolution Diffusion Models each edge (u,v) has probability puv / weight wuv initially, some nodes are active (e.g., a, d, e, g, i) SFU, CMPT 741, Fall 2009, Martin Ester

Graph Evolution Diffusion Models Threshold model [Granovetter 78] each node has a threshold t node u is activated when where active(u) are the active neighbors of u deterministic activation Independent contagion model [Dodds & Watts 2004] when node u becomes active, it activates each of its neighbors v with probability puv a node has only one chance to influence its neighbors - probabilistic activation SFU, CMPT 741, Fall 2009, Martin Ester

Social Network Analysis
Viral Marketing Customers becoming less susceptible to mass marketing Mass marketing impractical for unprecedented variety of products online Viral marketing successfully utilizes social networks for marketing products and services We are more influenced by our friends than strangers 68% of consumers consult friends and family before purchasing home electronics (Burke 2003) E.g., Hotmail gains 18 million users in 12 months, spending only $50,000 on traditional advertising SFU, CMPT 741, Fall 2009, Martin Ester

Most Influential Nodes [Kempe et al 2003] S: initial active node set f(S): expected size of final active set Most influential set of size k: the set S of k nodes producing largest f(S), if activated SFU, CMPT 741, Fall 2009, Martin Ester

Most Influential Nodes Can use various diffusion models Diminishing returns: pv(u,S) ≥ pv(u,T) if S ⊆T where pv(u,S) denotes the marginal gain of f(S) when adding u to S Independent contagion model has diminishing returns Greedy algorithm repeatedly select node with maximum marginal gain Performance guarantee solution of greedy algorithm is within (1‐1/e) ~63% of optimal solution Reason: f is submodular f submodular: if S ⊆T then f(S∪{x}) –f(S) ≥ f(T∪{x}) –f(T) SFU, CMPT 741, Fall 2009, Martin Ester

Viral Marketing Probability of buying increases with the first 10 recommendations Diminishing returns for further recommendations (saturation) DVD purchases SFU, CMPT 741, Fall 2009, Martin Ester

Viral Marketing  Probability of joining community increases sharply with the first friends in the community  Absolute values of probabilities are very small LiveJournal community membership SFU, CMPT 741, Fall 2009, Martin Ester

Role of Communities Consider connectedness of friends E.g., x and y have both three friends in the community - x’s friends are independent - y’s friends are all connected Who is more likely to join the community? SFU, CMPT 741, Fall 2009, Martin Ester

Role of Communities Competing sociological theories Information argument [Granovetter 1973] unconnected friends give independent support Social capital argument [Coleman 1988] safety / trust advantage in having friends who know each other In LiveJournal, community joining probability increases with more connections among friends in community  Independent contagion model too simplistic for real life data SFU, CMPT 741, Fall 2009, Martin Ester

Trust-Based Recommendation
Introduction Collaborative filtering given a user-item rating matrix predict missing ratings by aggregating ratings of users with similar rating profiles  Standard method for recommender systems Online social networks Trust-based recommendation given additionally a trust (social) network aggregate ratings of trusted neighbors SFU, CMPT 741, Fall 2009, Martin Ester

Introduction Explore the trust network to find raters. Aggregate their ratings. Advantages: can better deal with cold start users Challenge the larger the distance, the noisier the ratings but low probability of finding rater at small distances SFU, CMPT 741, Fall 2009, Martin Ester

Introduction How far to go in the network? tradeoff between precision and recall Instead of distant neighbors with same item use near neighbor with similar item SFU, CMPT 741, Fall 2009, Martin Ester

TrustWalker Random walk-based method Start from source user u0. In step k, at node u: If u has rated i, return ru,i With probability Φu,i,k , random walk stops Randomly select item j rated by u and return ru,j . With probability 1- Φu,i,k , continue random walk to a direct neighbor of u. SFU, CMPT 741, Fall 2009, Martin Ester

TrustWalker Φu,i,k sim(i,j): similarity of target item i and item j rated by user u. k: the step of random walk SFU, CMPT 741, Fall 2009, Martin Ester

TrustWalker Prediction = expected value returned by random walk. SFU, CMPT 741, Fall 2009, Martin Ester

TrustWalker Special cases of TrustWalker Φu,i,k = 1 Random walk never starts. Item-based Collaborative Filtering. Φu,i,k = 0 Pure trust-based recommendation. Continues until finding the exact target item. Aggregates the ratings weighted by probability of reaching them. Existing methods approximate this. Confidence How confident is the prediction? SFU, CMPT 741, Fall 2009, Martin Ester

References R. Albert and A.L. Barabasi: Emergence of scaling in random networks, Science, 1999 Karsten M. Borgwardt, Hans-Peter Kriegel: Shortest-Path Kernels on Graphs, ICDM 2005 Karsten Borgwardt, Xifeng Yan: Graph Mining and Graph Kernels, Tutorial KDD 2008 Peter Sheridan Dodds and Duncan J.Watts: Universal Behavior in a Generalized Model of Contagion, Phys. Rev. Letters, 2004 P. Erdos and A. Renyi: On the evolution of random graphs, Publication of the Mathematical Institute of the Hungarian Acadamy of Science, 1960 K. Faust and S.Wasserman: Blockmodels: Interpretation and evaluation, Social Networks,14, 1992 M. Girvan and M. E. J. Newman: Community structure in social and biological networks, Natl. Acad. Sci. USA, 2002 SFU, CMPT 741, Fall 2009, Martin Ester

References (contd.) Mark Granovetter: Threshold Models of Collective Behavior, American Journal of Sociology, Vol. 83, No. 6, 1978 M. Jamali, M. Ester: TrustWaker: A Random Walk Model for Combining Trust-based and Item-based Recommendation, KDD 2009 H. Kashima,K. Tsuda, and A. Inokuchi: Marginalized kernels between labeled graphs, ICML 2003 Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. & Ueda, N.: Learning systems of concepts with an infinite relational model, AAAI 2006 D. Kempe, J Kleinberg, É Tardos: Maximizing the spread of influence through a social network, KDD 2003 J.Kleinberg, S. R.Kumar, P.Raghavan, S.Rajagopalan and A.Tomkins: The web as a graph: Measurements, models and methods, COCOON 1998 Jure Leskovec and Christos Faloutsos: Mining Large Graphs, Tutorial ECML/PKDD 2007 SFU, CMPT 741, Fall 2009, Martin Ester

References (contd.) F. Moser, R. Colak, A. Rafiey, and M. Ester: Mining cohesive patterns from graphs with feature vectors, SDM 2009 M. E. J. Newman: Fast algorithm for detecting community structure in networks, Phys. Rev. E 69, 2004 Jian Pei, Daxin Jiang, Aidong Zhang: On Mining CrossGraph QuasiCliques, KDD 2005 Jianbo Shi and Jitendra Malik: Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, 2000 SFU, CMPT 741, Fall 2009, Martin Ester

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