An efficient graph-mining method for complicated and noisy data with real-world applications

Jia, Yang; Zhang, Jintao; Huan, Jun

doi:10.1007/s10115-010-0376-y

Cited by 55 publications

(39 citation statements)

References 24 publications

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“…Within this framework, a subgraph is considered anomalous if it is infrequent or parts of it are rarely repeated in the analyzed network. Jia et al [16] introduce a framework for mining interesting or anomalous patterns and subgraphs out of noisy and distorted graphs. Eberle et al [11] consider a substructure as anomalous if it deviates from a "normative" substructure, discovered by compression, based on the MDL principle.…”

Section: Related Workmentioning

confidence: 99%

NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks

Bogdanov

Faloutsos

Mongiovì

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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How to spot and summarize anomalies in dynamic networks such as road networks, communication networks and social networks? An anomalous event, such as a traffic accident, a denial of service attack or a chemical spill, can affect several near-by edges and make them behave abnormally, over several consecutive time-ticks. We focus on spotting and summarizing such significant anomalous regions, spanning space (i.e. nearby edges), as well as time.Our first contribution is the problem formulation, namely finding all such Significant Anomalous Regions (SAR). The next contribution is the design of novel algorithms: an expensive, exhaustive algorithm, as well as an efficient approximation, called NetSpot. Compared to the exhaustive algorithm, NetSpot is up to one order of magnitude faster in real data, while achieving less than 4% average relative error rate. In synthetic datasets, it is more than 30 times faster and solves large problem instances that are otherwise infeasible. The final contribution is the validation on real data: we demonstrate the utility of NetSpot for inferring accidents on road networks and detecting patterns of anomalous access to subnetworks of Wikipedia. We also study NetSpot's scalability in large social, transportation and synthetic evolving networks, spanning in total up to 50 million edges.

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Section: Related Workmentioning

confidence: 99%

NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks

Bogdanov

Faloutsos

Mongiovì

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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“…Graph mining [1,91,44] is closely related to subtree mining, in fact, tree mining can be viewed as a special case of graph mining. Reference [1] gives a comprehensive survey on the topic.…”

Section: Graph Mining and Itemset Miningmentioning

confidence: 99%

EvoMiner: frequent subtree mining in phylogenetic databases

et al. 2013

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The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the wellknown maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees ---two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set. Disciplines Theory and AlgorithmsThis article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/cs_techreports/11Noname manuscript No. The final publication is available at http://link.springer.com Abstract The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like level-wise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest common ancestor based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speed-ups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth first enumeration mode, to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority rule trees -two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.

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“…This approach is not feasible for large graphs with label multiplicities as there are potentially an exponential number of isomorphisms [2]. In [13], they proposed APGM to mine approximate frequent subgraphs from a database of graphs. The method is similar to the gApprox method, with the main difference being that the entire 1-hop neighborhood of the current embeddings is explored to enumerate all extensions of the frequent pattern and their corresponding embeddings, whereas gApprox enumerates the embeddings for a single extension in each step.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…What is required is a new class of algorithms that can mine frequent approximate patterns via approximate subgraph isomorphism that satisfies some bound on the overall cost of the match between a candidate and the database graph(s). Only a few methods have tackled this problem [5,13,20], but they typically enumerate all isomorphisms, and are therefore not scalable to large graphs due to the combinatorial explosion in the number of isomorphisms.…”

Section: Introductionmentioning

confidence: 99%

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Approximate graph mining with label costs

Anchuri¹,

Zaki²,

Barkol³

et al. 2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

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An efficient graph-mining method for complicated and noisy data with real-world applications

Cited by 55 publications

References 24 publications

NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks

NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks

EvoMiner: frequent subtree mining in phylogenetic databases

Approximate graph mining with label costs

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