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University of Illinois at Urbana-Champaign Graph Indexing: Tree + Δ ≥ Graph Peixiang Zhao Jeffrey Xu Yu Philip S. Yu Peixiang Zhao Jeffrey Xu Yu Philip S. Yu CS@UIUC SEEM@CUHK IBM T. J. Watson Research Center CS@UIUC SEEM@CUHK IBM T. J. Watson Research Center September 12 th, 2007 VLDB’07 Vienna, Austria

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 2 of 25 Synopsis IntroductionIntroduction Graph Containment Query Algorithmic Framework Related WorkRelated Work Tree + ΔTree + Δ Indexability of frequent Trees Discriminative graph feature selection: Δ Experimental StudyExperimental Study ConclusionConclusion

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 3 of 25 Introduction Graph is a mathematical construct and a general data structure representing relations among entitiesGraph is a mathematical construct and a general data structure representing relations among entities The emergence and the dominance of graphs asks for effective graph data management and mining tools so that users can organize, access, and analyze graph data efficientlyThe emergence and the dominance of graphs asks for effective graph data management and mining tools so that users can organize, access, and analyze graph data efficiently Structural Pattern Mining: Given a graph database, what are the potentially interesting structural patterns and how can we find them? Graph Indexing and Search: How can we index graphs and perform searching, either exactly or approximately, in large graph databases?

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 4 of 25 Introduction Graph Containment QueryGraph Containment Query Given a graph database G = {g 1, g 2, …, g N } and a query graph q, find the set NP, since subgraph-isomorphism checking is NP-Complete Infeasible to check subgraph isomorphism sequentially for every g i in G, especially challenging when graphs in G are large, or G is large and diverse Graph indexing!

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 5 of 25 Graph Indexing: Algorithmic Framework Index construction generates the index feature set F from the graph database G. For each feature f, sup(f) is maintainedIndex construction generates the index feature set F from the graph database G. For each feature f, sup(f) is maintained Query processing is performed in a filtering-verification fashion:Query processing is performed in a filtering-verification fashion: The filtering phase uses indexing features contained in q to compute the candidate answer set Every graph in C q contains all q's indexed features. Therefore, the query answer set, sup(q), is a subset of C q The verification phase checks subgraph isomorphism for every graph in C q. False positives are pruned and the true answer set sup(q) is returned

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 6 of 25 Query Cost Model The cost of processing a graph containment query q upon G, denoted C, can be modeled as belowThe cost of processing a graph containment query q upon G, denoted C, can be modeled as below C f : the filtering cost C v : the verification cost (NP-Complete) AnalysisAnalysis 1.The key issue to improve query performance is to minimize |C q | 2.The indexing feature set F is quite relevant to C f and |C q | 3.Index construction performance: the feature selection cost C fs to construct F from among G

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 7 of 25 Related Work Path-based Indexing approachPath-based Indexing approach All existing paths up to a certain length lp are enumerated as indexing features –Index can be constructed efficiently –Index size is quite large when lp is not small –Limited pruning power, mainly because the structural information exhibited in graphs is lost when breaking graphs into paths GraphGrep ( PODS’02 ) Graph-based Indexing approachGraph-based Indexing approach Subgraphs of G with different characteristics are selected as indexing features –A costly index construction process –Compact index structure –Great pruning power, since structural information of graph is well-preserved gIndex ( SIGMOD’04, PODS’05 ), C-Tree ( ICDE’06 ), GString ( ICDE’07 ), GDIndex ( ICDE’07 ), FG-Index ( SIGMOD’07 )

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 8 of 25 An alternative approach: (Tree + Δ) Tree-based Graph IndexingTree-based Graph Indexing Tree: Better indexability in comparison with path and graph –The majority of frequent graph-features of G are usually tree-features indeed –Frequent tree-features and graph-features share similar distributions and frequent tree-features have similar pruning power like graph-features –tree mining can be done much more efficiently than graph mining on G Δ : On-demand select a small number of discriminative graph-features without conducting costly graph mining beforehand Orders of magnitude smaller in index size, but performs much better than existing approaches in indexing construction and query processing

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 9 of 25 Indexability of Path, Tree and Graph Frequent features (paths, trees, graphs) expose intrinsic characteristics of a graph database, G. They are representatives to discriminate between different groups of graphs in a graph database Which one should we index? Path, Tree or Graph? 1.The frequent feature set size: | F | 2.The feature selection cost: C fs 3.the candidate answer set size: |C q |

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 10 of 25 The Frequent Feature Set Size:F The Frequent Feature Set Size: | F | Evidences:Evidences: Among all frequent graph-features of G, a majority of them are trees indeed –All subtrees of a frequent graph are frequent –There is little chance that subtrees of frequent graph g coincide with those of frequent graph g ’, due to the structural diversity and label variety Frequent paths share a very small portion, because a path-feature has a simple linear structure, which has little variety in structural complexity In terms of feature distributions, tree-features and graph-features share a very similar distribution w.r.t. feature size

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 11 of 25 Experiments on Two Datasets w.r.t. F Experiments on Two Datasets w.r.t. | F | The Real Dataset The Synthetic Dataset

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 12 of 25 The feature selection cost: C fs Given a graph database, G, and a minimum support threshold, σ, to discover the frequent feature set F (F P / F T / F G ) from G Tree A good compromise between –the more expressive, but computationally harder general graph –the faster but less expressive path Specialization of general graph avoiding undesirable theoretical properties and algorithmic complexity incurred by graph PathTreeGraph Isomorphism O(n) P or NPC (?) Sub-Isomorphism O(n + m) O(m 3/2 n/logm) NP-Complete

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 13 of 25 The Candidate Answer Set Size: |C q | We define the pruning power power(f) of a frequent feature f as The pruning power of a frequent feature set S = {f 1, f 2, …, f n } Theorem 1: Given a frequent graph-feature g, and let its frequent sub- tree set be T (g) = {t 1, t 2, …, t n }. Then, power(g) ≥ power(T (g)) Theorem 2: Given a frequent tree-feature t, and let its frequent sub-path set be P (t) = {p 1, p 2, …, p m }. Then, power(t) ≥ power(P (t))

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 14 of 25 Pruning Power The pruning power of all frequent subtree features, T (g), of a frequent graph-feature g can be similar to the pruning power of g There is a big gap between the pruning power of a graph- feature g and that of all its frequent sub-path features, P(g) The Real Dataset

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 15 of 25 Indexability of Path, Tree and Graph It is feasible and effective to select F T, instead of F G, as indexing features for the graph containment query problem The frequent tree-feature set, F T, dominates F G Discovering frequent tree-features from G can be done much more efficiently than mining frequent general graph-features F T can contribute similar pruning power like that provided by F G

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 16 of 25 Discriminative Graph Features Consider a query graph q which contains a subgraph g If power(T (g)) ≈ power(g), there is no need to index the graph-feature g, because its subtrees jointly have the similar pruning power if power(g) >> power(T (g)), it will be necessary to select g as an index feature because g is more discriminative than T (g), in terms of pruning Discriminative graph-features (w.r.t. its subtree-features, controlled by ε 0 ) are selected from queries on-demand, without mining the whole set of frequent graph-features from G beforehand Discriminative graph-features are used as additional indexing features, denoted Δ, which can also be reused further to answer subsequent queries Δ

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 17 of 25 Discriminative Graph Selection Given two graphs g, g’ q, where g g’ If the gap between power(g’) and power(g) is large enough, g’ will be reclaimed from G; Otherwise, g is discriminative enough for pruning purpose, and there is no need to reclaim g’ in the presence of g Approximate the discriminative computation between g’ and g, in the presence of our knowledge on frequent tree-features discovered

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 18 of 25 Discriminative Graph Selection The occurrence probability of g in the graph database, G the conditional occurrence probability of g’, w.r.t. g, models the probability to select g’ from G in the presence of g The upper and lower bound of Pr(g’|g) The conditional occurrence probability of Pr(g’|g), is solely upper-bounded by T (g’)

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 19 of 25 Experimental Studies The Real DatasetThe Real Dataset The AIDS antiviral screen dataset from Developmental Theroapeutics Program in NCI/NIH 42390 compounds retrieved from DTP's Drug Information System 63 kinds of atoms in this dataset, most of which are C, H, O, S, etc. Three kinds of bonds are popular in these compounds: single-bond, double-bond and aromatic-bond On average, compounds in the dataset has 43 vertices and 45 edges. The graph of maximum size has 221 vertices and 234 edges

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 20 of 25 Experimental Studies The real dataset: index constructionThe real dataset: index construction

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 21 of 25 Experimental Studies The real dataset: false positive ratio (|Cq|/|sup(q)|) w.r.t. the database size (= 1,000; 2,000; 4,000; 8,000; 10,000)The real dataset: false positive ratio (|Cq|/|sup(q)|) w.r.t. the database size (= 1,000; 2,000; 4,000; 8,000; 10,000)

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 22 of 25 Experimental Studies The Synthetic DatasetThe Synthetic Dataset Generated by a widely-used graph generator, which is controlled by the following parameters :

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 23 of 25 Experimental Studies The synthetic dataset: false positive ratioThe synthetic dataset: false positive ratio

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Sept. 12 th, 2007 VLDB’07 Vienna, Austria 24 of 25 Conclusion Graph indexing plays a critical role in graph containment query processing on large graph databases Path-based and graph-based indexing approaches suffer from overly large index size, substantial index construction overhead and expensive query processing cost (Tree+Δ) is an effective and efficient graph indexing feature to answer graph containment queries (Tree+Δ) holds a compact index structure, achieves good performance in index construction and most importantly, provides satisfactory query performance for answering graph containment queries over large graph databases

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University of Illinois at Urbana-Champaign Thank you VLDB’07 Vienna, Austria

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