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Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining
[chapter]
2009
Lecture Notes in Computer Science
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Thus, to improve its efficiency, we propose pruning methods based on the upper-bound of information gain that is used as a criterion for discriminability of subgraphs in Cl-GBI. ...
The upper-bound of information gain of a subgraph is the maximal one that its super graph can achieve. ...
This work was partly supported by a grant-in-aid for Young Scientists (B) No. 19700145 from MEXT, Japan. ...
doi:10.1007/978-3-642-01715-5_5
fatcat:casamhyr5va3rk524nm3gl2nqu
LTS: Discriminative subgraph mining by learning from search history
2011
2011 IEEE 27th International Conference on Data Engineering
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We discover that search history of discriminative subgraph mining is very useful in computing empirical upper-bounds of discrimination scores of subgraphs. ...
The search space for discriminative subgraphs is usually prohibitively large. ...
ACKNOWLEDGMENT We thank Xifeng Yan and Sayan Ranu for their kindly providing implementation of LEAP and graphSig. ...
doi:10.1109/icde.2011.5767922
dblp:conf/icde/JinW11
fatcat:eym7suj6bbejfixq3nee4ok3k4
Mining significant graph patterns by leap search
2008
Proceedings of the 2008 ACM SIGMOD international conference on Management of data - SIGMOD '08
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Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. ...
Furthermore, graph classifiers built on mined patterns outperform the up-to-date graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns. ...
[5] derived a frequency upper bound of discriminative measures such as information gain and Fisher score, showing a relationship between frequency and discriminative measures. ...
doi:10.1145/1376616.1376662
dblp:conf/sigmod/YanCHY08
fatcat:jpmfkdtzsnfj7g2ozmcupbqcnu
Iterative Graph Feature Mining for Graph Indexing
2012
2012 IEEE 28th International Conference on Data Engineering
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Next, we propose a basic branch and bound algorithm to mine the features. ...
Most of the "mine-at-once" algorithms involve frequent subgraph or subtree mining over the whole graph database. ...
Fig. 2 . 2 One counterexample for the anti-monotonic of minSup(p, Q) Statement 4 (Branch Upper Bound): For a feature p, a branch upper bound exists such that ∀p ⊃ p, gain(p )
Fig. 3 . 3 Graph features ...
doi:10.1109/icde.2012.11
dblp:conf/icde/YuanMYG12
fatcat:rqnckyclkffxbjrhhr7yvlo4ii
Discriminative frequent subgraph mining with optimality guarantees
2010
Statistical analysis and data mining
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Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs ...
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. ...
The responsibility for this publication lies with the authors. The authors would like to thank Siegfried Nijssen for fruitful discussions. ...
doi:10.1002/sam.10084
fatcat:zodsnci3azcypaag7d7coh4xeu
Near-optimal supervised feature selection among frequent subgraphs
[chapter]
2009
Proceedings of the 2009 SIAM International Conference on Data Mining
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Second, our submodular quality function criterion can be integrated into gSpan, the state-of-the-art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs ...
On large graphs, however, one faces the enormous problem that the number of these frequent subgraphs may grow exponentially with the size of the graphs, but only few of them possess enough discriminative ...
(2.8) to provide an upper bound for the CORK values of supergraphs of a given subgraph S and exploit this information for pruning the search space in a branch-and-bound fashion. ...
doi:10.1137/1.9781611972795.92
dblp:conf/sdm/ThomaCGHKSSYYB09
fatcat:b5duyrszofgexo6djtvgxnzpje
Semi-supervised feature selection for graph classification
2010
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10
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Then we propose a branch-and-bound algorithm to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space. ...
the subgraph feature mining process. ...
However, when there are not enough labeled graphs, the pruning ability of the upper-bound based on labeled graphs can be poor, thus making it infeasible to find discriminative subgraph features within ...
doi:10.1145/1835804.1835905
dblp:conf/kdd/KongY10
fatcat:2kiysijawrcehjkokvjalz3kzi
Mining Top-K Graph Patterns that Jointly Maximize Some Significance Measure
2010
Journal of Computers
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The pruning condition for the patterns T is not empty based on Theorem 6 and 7. In
information gain measure is shown in Theorem 7. ...
For later iterations of greedy selection, we can define a In Line 1 of Function MiningNextPattern, p≠min(p)
similar upper bound for pruning. ...
doi:10.4304/jcp.5.4.565-572
fatcat:jyxhtwfbivdtxflqllmutb2cwi
On the Usefulness of Weight-Based Constraints in Frequent Subgraph Mining
[chapter]
2010
Research and Development in Intelligent Systems XXVII
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In particular, we study frequent subgraph mining in the presence of weight-based constraints and explain how to integrate them into mining algorithms. ...
A lower bound predicate c l for a pattern p is a predicate with the following structure: An upper bound predicate c u in turn is as follows: c u (p) := ( e 2 ∈ E(p) : measure(e 2 ) > t u ) ∨ (|p| < size ...
Acknowledgments We thank Zahir Balaporia (Schneider National, Inc.) and Chris Clifton (Purdue University) for providing us with the logistics dataset [9] . ...
doi:10.1007/978-0-85729-130-1_5
dblp:conf/sgai/EichingerHB10
fatcat:etdvl2r3hnfzblll3hoabjsuzi
Semi-supervised Clustering of Graph Objects: A Subgraph Mining Approach
[chapter]
2012
Lecture Notes in Computer Science
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We derive an upper bound of the objective function based on which, a branch-and-bound algorithm is proposed to speedup subgraph mining. ...
As there is no predefined feature set for the graph objects, we propose to use discriminative subgraph patterns as the features. ...
With the derived upper boundq, we can develop a branch-and-bound subgraph mining algorithm on top of gSpan for mining the optimal subgraph feature set T * . ...
doi:10.1007/978-3-642-29038-1_16
fatcat:6bahaz6rbfcjnmtyyfdo2pcga4
Multi-graph-view subgraph mining for graph classification
2015
Knowledge and Information Systems
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Specifically, we derive an evaluation criterion to estimate the discriminative power and redundancy of subgraph features across all views, with a branch-and-bound algorithm being proposed to prune subgraph ...
To solve the problem, we propose a Cross Graph-View Subgraph Feature based Learning (gCGVFL) algorithm that explores an optimal set of subgraphs, across multiple graph-views, as features to represent graphs ...
For example, on multi-graph-view Image dataset as shown in Figure 10 (B), UgCGVFL needs about 9000ms to mine the discriminative subgraphs, whereas by using the upper bound pruning gCGVFL only takes about ...
doi:10.1007/s10115-015-0872-1
fatcat:fjfwjnhmsbfrhddxyprgwoarlu
Multi-label Feature Selection for Graph Classification
2010
2010 IEEE International Conference on Data Mining
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subgraph feature mining process. ...
Then a branch-andbound algorithm is proposed to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space using multiple labels. ...
We further observe that in all tasks and evaluation criteria, our multi-label feature selection algorithm with multilabel classification (gMLC+BOOSTEXTER) outperforms the ...
doi:10.1109/icdm.2010.58
dblp:conf/icdm/KongY10
fatcat:wtqcozabinebzcg76bclstqx7e
Multi-graph-view Learning for Graph Classification
2014
2014 IEEE International Conference on Data Mining
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In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. ...
Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification. ...
Acknowledgments The work was supported by Australian Research Council (ARC) Discovery Projects under Grant No. DP140100545 and DP140102206. ...
doi:10.1109/icdm.2014.97
dblp:conf/icdm/WuHPZCZ14
fatcat:2ax3yvc4mjg3nhw4uh6kwuj63i
Dual active feature and sample selection for graph classification
2011
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11
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Current research on graph classification focuses on mining discriminative subgraph features under supervised settings. The basic assumption is that a large number of labeled graphs are available. ...
To address this challenge, we demonstrate how one can simultaneously estimate the usefulness of a query graph and a set of subgraph features. ...
ACKNOWLEDGEMENTS The work is supported in part by NSF through grants IIS-0905215, DBI-0960443, OISE-0968341 and OIA-0963278. ...
doi:10.1145/2020408.2020511
dblp:conf/kdd/KongFY11
fatcat:qcsfmkyz5fbldbvz5kbo6nmcdm
Efficient mining for structurally diverse subgraph patterns in large molecular databases
2010
Machine Learning
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This is confirmed experimentally by running times reduced by more than 60% compared to ordinary (static) upper bound pruning. ...
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. ...
This upper bound has the antimonotonic property and can thus be used for pruning the refinements of a structure, if their upper bound falls below a given threshold. ...
doi:10.1007/s10994-010-5187-6
fatcat:7h2zyhn2gfasdlobkg5re4nbsq
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