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  1. Transactions on Database Systems (TODS)
  2. ACM Transactions on Database Systems (TODS) : Volume 39
  3. Issue 4(Invited Articles Issue, SIGMOD 2013, PODS 2013 and ICDT 2013), December 2014
  4. The Complexity of Mining Maximal Frequent Subgraphs
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ACM Transactions on Database Systems (TODS) : Volume 42
ACM Transactions on Database Systems (TODS) : Volume 41
ACM Transactions on Database Systems (TODS) : Volume 40
ACM Transactions on Database Systems (TODS) : Volume 39
Issue 4(Invited Articles Issue, SIGMOD 2013, PODS 2013 and ICDT 2013), December 2014
Foreword to Invited Articles Issue
I/O-Efficient Algorithms on Triangle Listing and Counting
Discovering XSD Keys from XML Data
A Scalable Lock Manager for Multicores
Lightweight Query Authentication on Streams
Naïve Evaluation of Queries over Incomplete Databases
The Complexity of Mining Maximal Frequent Subgraphs
Ontology-Based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP
A Theory of Pricing Private Data
Top-k and Clustering with Noisy Comparisons
Issue 3, September 2014
Issue 2, May 2014
Issue 1, January 2014
ACM Transactions on Database Systems (TODS) : Volume 38
ACM Transactions on Database Systems (TODS) : Volume 37
ACM Transactions on Database Systems (TODS) : Volume 36
ACM Transactions on Database Systems (TODS) : Volume 35
ACM Transactions on Database Systems (TODS) : Volume 34
ACM Transactions on Database Systems (TODS) : Volume 33
ACM Transactions on Database Systems (TODS) : Volume 32
ACM Transactions on Database Systems (TODS) : Volume 31
ACM Transactions on Database Systems (TODS) : Volume 30
ACM Transactions on Database Systems (TODS) : Volume 29
ACM Transactions on Database Systems (TODS) : Volume 28
ACM Transactions on Database Systems (TODS) : Volume 27
ACM Transactions on Database Systems (TODS) : Volume 26
ACM Transactions on Database Systems (TODS) : Volume 25
ACM Transactions on Database Systems (TODS) : Volume 24
ACM Transactions on Database Systems (TODS) : Volume 23
ACM Transactions on Database Systems (TODS) : Volume 22
ACM Transactions on Database Systems (TODS) : Volume 21
ACM Transactions on Database Systems (TODS) : Volume 20
ACM Transactions on Database Systems (TODS) : Volume 19
ACM Transactions on Database Systems (TODS) : Volume 18
ACM Transactions on Database Systems (TODS) : Volume 17
ACM Transactions on Database Systems (TODS) : Volume 16
ACM Transactions on Database Systems (TODS) : Volume 15
ACM Transactions on Database Systems (TODS) : Volume 14
ACM Transactions on Database Systems (TODS) : Volume 13
ACM Transactions on Database Systems (TODS) : Volume 12
ACM Transactions on Database Systems (TODS) : Volume 11
ACM Transactions on Database Systems (TODS) : Volume 10
ACM Transactions on Database Systems (TODS) : Volume 9
ACM Transactions on Database Systems (TODS) : Volume 8
ACM Transactions on Database Systems (TODS) : Volume 7
ACM Transactions on Database Systems (TODS) : Volume 6
ACM Transactions on Database Systems (TODS) : Volume 5
ACM Transactions on Database Systems (TODS) : Volume 4
ACM Transactions on Database Systems (TODS) : Volume 3
ACM Transactions on Database Systems (TODS) : Volume 2
ACM Transactions on Database Systems (TODS) : Volume 1

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The Complexity of Mining Maximal Frequent Subgraphs

Content Provider ACM Digital Library
Author Kolaitis, Phokion G. Kimelfeld, Benny
Copyright Year 2014
Abstract A frequent subgraph of a given collection of graphs is a graph that is isomorphic to a subgraph of at least as many graphs in the collection as a given threshold. Frequent subgraphs generalize frequent itemsets and arise in various contexts, from bioinformatics to the Web. Since the space of frequent subgraphs is typically extremely large, research in graph mining has focused on special types of frequent subgraphs that can be orders of magnitude smaller in number, yet encapsulate the space of all frequent subgraphs. $\textit{Maximal}$ frequent subgraphs (i.e., the ones not properly contained in any frequent subgraph) constitute the most useful such type. In this article, we embark on a comprehensive investigation of the computational complexity of mining maximal frequent subgraphs. Our study is carried out by considering the effect of three different parameters: possible restrictions on the class of graphs; a fixed bound on the threshold; and a fixed bound on the number of desired answers. We focus on specific classes of connected graphs: general graphs, planar graphs, graphs of bounded degree, and graphs of bounded treewidth (trees being a special case). Moreover, each class has two variants: that in which the nodes are unlabeled, and that in which they are uniquely labeled. We delineate the complexity of the enumeration problem for each of these variants by determining when it is solvable in (total or incremental) polynomial time and when it is NP-hard. Specifically, for the labeled classes, we show that bounding the threshold yields tractability but, in most cases, bounding the number of answers does not, unless P=NP; an exception is the case of labeled trees, where bounding either of these two parameters yields tractability. The state of affairs turns out to be quite different for the unlabeled classes. The main (and most challenging to prove) result concerns unlabeled trees: we show NP-hardness, even if the input consists of two trees and both the threshold and the number of desired answers are equal to just two. In other words, we establish that the following problem is NP-complete: given two unlabeled trees, do they have more than one maximal subtree in common?
Starting Page 1
Ending Page 33
Page Count 33
File Format PDF
ISSN 03625915
e-ISSN 15574644
DOI 10.1145/2629550
Volume Number 39
Issue Number 4
Journal ACM Transactions on Database Systems (TODS)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-12-30
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Graph mining Enumeration complexity Maximal frequent subgraphs
Content Type Text
Resource Type Article
Subject Information Systems
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