3,993 research outputs found
GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search
The mining of frequent subgraphs from labeled graph data has been studied
extensively. Furthermore, much attention has recently been paid to frequent
pattern mining from graph sequences. A method, called GTRACE, has been proposed
to mine frequent patterns from graph sequences under the assumption that
changes in graphs are gradual. Although GTRACE mines the frequent patterns
efficiently, it still needs substantial computation time to mine the patterns
from graph sequences containing large graphs and long sequences. In this paper,
we propose a new version of GTRACE that enables efficient mining of frequent
patterns based on the principle of a reverse search. The underlying concept of
the reverse search is a general scheme for designing efficient algorithms for
hard enumeration problems. Our performance study shows that the proposed method
is efficient and scalable for mining both long and large graph sequence
patterns and is several orders of magnitude faster than the original GTRACE
Reductions for Frequency-Based Data Mining Problems
Studying the computational complexity of problems is one of the - if not the
- fundamental questions in computer science. Yet, surprisingly little is known
about the computational complexity of many central problems in data mining. In
this paper we study frequency-based problems and propose a new type of
reduction that allows us to compare the complexities of the maximal frequent
pattern mining problems in different domains (e.g. graphs or sequences). Our
results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader
range of data mining problems. Our results show that, by allowing constraints
in the pattern space, the complexities of many maximal frequent pattern mining
problems collapse. These problems include maximal frequent subgraphs in
labelled graphs, maximal frequent itemsets, and maximal frequent subsequences
with no repetitions. In addition to theoretical interest, our results might
yield more efficient algorithms for the studied problems.Comment: This is an extended version of a paper of the same title to appear in
the Proceedings of the 17th IEEE International Conference on Data Mining
(ICDM'17
FS^3: A Sampling based method for top-k Frequent Subgraph Mining
Mining labeled subgraph is a popular research task in data mining because of
its potential application in many different scientific domains. All the
existing methods for this task explicitly or implicitly solve the subgraph
isomorphism task which is computationally expensive, so they suffer from the
lack of scalability problem when the graphs in the input database are large. In
this work, we propose FS^3, which is a sampling based method. It mines a small
collection of subgraphs that are most frequent in the probabilistic sense. FS^3
performs a Markov Chain Monte Carlo (MCMC) sampling over the space of a
fixed-size subgraphs such that the potentially frequent subgraphs are sampled
more often. Besides, FS^3 is equipped with an innovative queue manager. It
stores the sampled subgraph in a finite queue over the course of mining in such
a manner that the top-k positions in the queue contain the most frequent
subgraphs. Our experiments on database of large graphs show that FS^3 is
efficient, and it obtains subgraphs that are the most frequent amongst the
subgraphs of a given size
Enumerating Maximal Bicliques from a Large Graph using MapReduce
We consider the enumeration of maximal bipartite cliques (bicliques) from a
large graph, a task central to many practical data mining problems in social
network analysis and bioinformatics. We present novel parallel algorithms for
the MapReduce platform, and an experimental evaluation using Hadoop MapReduce.
Our algorithm is based on clustering the input graph into smaller sized
subgraphs, followed by processing different subgraphs in parallel. Our
algorithm uses two ideas that enable it to scale to large graphs: (1) the
redundancy in work between different subgraph explorations is minimized through
a careful pruning of the search space, and (2) the load on different reducers
is balanced through the use of an appropriate total order among the vertices.
Our evaluation shows that the algorithm scales to large graphs with millions of
edges and tens of mil- lions of maximal bicliques. To our knowledge, this is
the first work on maximal biclique enumeration for graphs of this scale.Comment: A preliminary version of the paper was accepted at the Proceedings of
the 3rd IEEE International Congress on Big Data 201
Mining Frequent Graph Patterns with Differential Privacy
Discovering frequent graph patterns in a graph database offers valuable
information in a variety of applications. However, if the graph dataset
contains sensitive data of individuals such as mobile phone-call graphs and
web-click graphs, releasing discovered frequent patterns may present a threat
to the privacy of individuals. {\em Differential privacy} has recently emerged
as the {\em de facto} standard for private data analysis due to its provable
privacy guarantee. In this paper we propose the first differentially private
algorithm for mining frequent graph patterns.
We first show that previous techniques on differentially private discovery of
frequent {\em itemsets} cannot apply in mining frequent graph patterns due to
the inherent complexity of handling structural information in graphs. We then
address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling
based algorithm. Unlike previous work on frequent itemset mining, our
techniques do not rely on the output of a non-private mining algorithm.
Instead, we observe that both frequent graph pattern mining and the guarantee
of differential privacy can be unified into an MCMC sampling framework. In
addition, we establish the privacy and utility guarantee of our algorithm and
propose an efficient neighboring pattern counting technique as well.
Experimental results show that the proposed algorithm is able to output
frequent patterns with good precision
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