
diff -r bdf97dafabfb -r 42fbe17f0e3b damecco.tex
--- a/damecco.tex	Wed Nov 30 22:45:35 2016 +0100
+++ b/damecco.tex	Wed Nov 30 23:03:41 2016 +0100
@@ -103,46 +103,60 @@
 %% \address{Address\fnref{label3}}
 %% \fntext[label3]{}
 
-\title{VF2++ --- An Improved Subgraph Isomorphism Algorithm}
+\title{Improved Algorithms for Matching Biological Graphs}
 
 %% use optional labels to link authors explicitly to addresses:
 %% \author[label1,label2]{}
 %% \address[label1]{}
 %% \address[label2]{}
 
-\author[egres,elte]{Alp{\'a}r J{\"u}ttner}
-\ead{alpar@cs.elte.hu}
-\author[elte]{P{\'e}ter Madarasi}
-\ead{madarasip@caesar.elte.hu}
-\address[egres]{MTA-ELTE Egerv{\'a}ry Research Group, Budapest, Hungary.}
-\address[elte]{Department of Operations Research, E{\"o}tv{\"o}s Lor{\'a}nd University, Budapest, Hungary.}
+\author{Alp{\'a}r J{\"u}ttner and P{\'e}ter Madarasi}
+
+\address{Dept of Operations Research, ELTE}
 
 \begin{abstract}
+Subgraph isomorphism is a well-known NP-Complete problem, while its
+special case, the graph isomorphism problem is one of the few problems
+in NP neither known to be in P nor NP-Complete. Their appearance in
+many fields of application such as pattern analysis, computer vision
+questions and the analysis of chemical and biological systems has
+fostered the design of various algorithms for handling special graph
+structures.
 
-  This paper presents a largely improved version of the VF2 algorithm
-  for the \emph{Subgraph Isomorphism Problem}. The improvements are
-  twofold. Firstly, it is based on a new approach for determining the
-  matching order of the nodes, and secondly, more efficient -
-  nevertheless easier to compute - cutting rules significantly
-  reducing the search space are applied.
+This paper presents VF2++, a new algorithm based on the original VF2,
+which runs significantly faster on most test cases and performs
+especially well on special graph classes stemming from biological
+questions. VF2++ handles graphs of thousands of nodes in practically
+near linear time including preprocessing. Not only is it an improved
+version of VF2, but in fact, it is by far the fastest existing
+algorithm especially on biological graphs.
 
-  In addition to the usual subgraph isomorphism, the paper also
-  presents specialized versions for the \emph{Induced Subgraph
-    Isomorphism} and for the \emph{Graph Isomorphism Problems}.
+The reason for VF2++' superiority over VF2 is twofold. Firstly, taking
+into account the structure and the node labeling of the graph, VF2++
+determines a state order in which most of the unfruitful branches of
+the search space can be pruned immediately. Secondly, introducing more
+efficient - nevertheless still easier to compute - cutting rules
+reduces the chance of going astray even further.
 
-  Finally, an extensive experimental evaluation is provided using a
-  wide range of inputs, including both real life biological and
-  chemical datasets and standard randomly generated graph series. The
-  results show major and consistent running time improvements over the
-  other known methods.
- 
-  The C++ implementations of the algorithms are available open source as
-  the part of the LEMON graph and network optimization library.
-  
+In addition to the usual subgraph isomorphism, specialized versions
+for induced subgraph isomorphism and for graph isomorphism are
+presented. VF2++ has gained a runtime improvement of one order of
+magnitude respecting induced subgraph isomorphism and a better
+asymptotical behaviour in the case of graph isomorphism problem.
+
+After having provided the description of VF2++, in order to evaluate
+its effectiveness, an extensive comparison to the contemporary other
+algorithms is shown, using a wide range of inputs, including both real
+life biological and chemical datasets and standard randomly generated
+graph series.
+
+The work was motivated and sponsored by QuantumBio Inc., and all the
+developed algorithms are available as the part of the open source
+LEMON graph and network optimization library
+(http://lemon.cs.elte.hu).
 \end{abstract}
 
 \begin{keyword}
-  Computational Biology, Subgraph Isomorphism Problem
 %% keywords here, in the form: keyword \sep keyword
 
 %% PACS codes here, in the form: \PACS code \sep code
@@ -180,7 +194,7 @@
 edges to chemical bonds. The similarity and dissimilarity of
 objects corresponding to nodes are incorporated to the model
 by \emph{node labels}. Understanding such networks basically
-requires finding specific subgraphs, thus calls for efficient
+requires finding specific subgraphs, thus it calls for efficient
 graph matching algorithms.
 
 Other real-world fields related to some
@@ -255,12 +269,13 @@
 efficient cutting rules and determines a node order in which VF2 runs
 significantly faster on practical inputs.
 
+graph and network optimization library
+
 This project was initiated and sponsored by QuantumBio
-Inc.\cite{QUANTUMBIO} and the implementation --- along with a source
-code --- has been published as a part of LEMON\cite{LEMON} open source
-graph library.
+Inc.\cite{QUANTUMBIO} and the implementation is available open source as the 
+part of the LEMON\cite{LEMON} graph and network optimization library.
 
-Outline: Section~\ref{sec:ProbStat} defines the problems to be solved, Section~\ref{sec:VF2Alg} provides a description of VF2, Section~\ref{sec:VF2ppAlg} introduces VF2++, a new graph matching algorithm, Section~\ref{sec:VF2ppImpl} presents the details of an efficient implementation of VF2++, and Section~\ref{sec:ExpRes} compares VF2++ to a state of the art algorithm.
+Outline: Section~\ref{sec:ProbStat} defines the problems to be solved, Section~\ref{sec:VF2Alg} provides a description of VF2, Section~\ref{sec:VF2ppAlg} introduces VF2++, a new graph matching algorithm, Section~\ref{sec:VF2ppImpl} presents the details of an efficient implementation of VF2++, and Section~\ref{sec:ExpRes} compares VF2++ to the state of the art algorithm.
 
 \section{Problem Statement}\label{sec:ProbStat}
 This section provides a formal description of the problems to be




