873 research outputs found
Graph theoretic methods for the analysis of structural relationships in biological macromolecules
Subgraph isomorphism and maximum common subgraph isomorphism algorithms from graph theory provide an effective and an efficient way of identifying structural relationships between biological macromolecules. They thus provide a natural complement to the pattern matching algorithms that are used in bioinformatics to identify sequence relationships. Examples are provided of the use of graph theory to analyze proteins for which three-dimensional crystallographic or NMR structures are available, focusing on the use of the Bron-Kerbosch clique detection algorithm to identify common folding motifs and of the Ullmann subgraph isomorphism algorithm to identify patterns of amino acid residues. Our methods are also applicable to other types of biological macromolecule, such as carbohydrate and nucleic acid structures
Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis
This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
Malware Classification based on Call Graph Clustering
Each day, anti-virus companies receive tens of thousands samples of
potentially harmful executables. Many of the malicious samples are variations
of previously encountered malware, created by their authors to evade
pattern-based detection. Dealing with these large amounts of data requires
robust, automatic detection approaches. This paper studies malware
classification based on call graph clustering. By representing malware samples
as call graphs, it is possible to abstract certain variations away, and enable
the detection of structural similarities between samples. The ability to
cluster similar samples together will make more generic detection techniques
possible, thereby targeting the commonalities of the samples within a cluster.
To compare call graphs mutually, we compute pairwise graph similarity scores
via graph matchings which approximately minimize the graph edit distance. Next,
to facilitate the discovery of similar malware samples, we employ several
clustering algorithms, including k-medoids and DBSCAN. Clustering experiments
are conducted on a collection of real malware samples, and the results are
evaluated against manual classifications provided by human malware analysts.
Experiments show that it is indeed possible to accurately detect malware
families via call graph clustering. We anticipate that in the future, call
graphs can be used to analyse the emergence of new malware families, and
ultimately to automate implementation of generic detection schemes.Comment: This research has been supported by TEKES - the Finnish Funding
Agency for Technology and Innovation as part of its ICT SHOK Future Internet
research programme, grant 40212/0
RASCAL: calculation of graph similarity using maximum common edge subgraphs
A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a minimum similarity threshold, the procedure consists of an initial screening process to determine whether it is possible for the measure of similarity between the two graphs to exceed the minimum threshold, followed by a rigorous maximum common edge subgraph (MCES) detection algorithm to compute the exact degree and composition of similarity. The proposed MCES algorithm is based on a maximum clique formulation of the problem and is a significant improvement over other published algorithms. It presents new approaches to both lower and upper bounding as well as vertex selection
Graph Homomorphism Revisited for Graph Matching
In a variety of emerging applications one needs to decide whether a graph
G matches
another
G
p
,
i.e.
, whether
G
has a topological structure similar to that of
G
p
. The traditional notions of graph homomorphism and isomorphism often fall short of capturing the structural similarity in these applications. This paper studies revisions of these notions, providing a full treatment from complexity to algorithms. (1) We propose
p-homomorphism (p
-hom) and 1-1
p
-hom, which extend graph homomorphism and subgraph isomorphism, respectively, by mapping
edges
from one graph to
paths
in another, and by measuring
the similarity of nodes
. (2) We introduce metrics to measure graph similarity, and several optimization problems for
p
-hom and 1-1
p
-hom. (3) We show that the decision problems for
p
-hom and 1-1
p
-hom are NP-complete even for DAGs, and that the optimization problems are approximation-hard. (4) Nevertheless, we provide approximation algorithms with
provable guarantees
on match quality. We experimentally verify the effectiveness of the revised notions and the efficiency of our algorithms in Web site matching, using real-life and synthetic data.
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