1.1 --- a/damecco.tex	Wed Nov 30 21:51:10 2016 +0100
     1.2 +++ b/damecco.tex	Wed Nov 30 22:45:35 2016 +0100
     1.3 @@ -103,60 +103,46 @@
     1.4  %% \address{Address\fnref{label3}}
     1.5  %% \fntext[label3]{}
     1.6  
     1.7 -\title{Improved Algorithms for Matching Biological Graphs}
     1.8 +\title{VF2++ --- An Improved Subgraph Isomorphism Algorithm}
     1.9  
    1.10  %% use optional labels to link authors explicitly to addresses:
    1.11  %% \author[label1,label2]{}
    1.12  %% \address[label1]{}
    1.13  %% \address[label2]{}
    1.14  
    1.15 -\author{Alp{\'a}r J{\"u}ttner and P{\'e}ter Madarasi}
    1.16 -
    1.17 -\address{Dept of Operations Research, ELTE}
    1.18 +\author[egres,elte]{Alp{\'a}r J{\"u}ttner}
    1.19 +\ead{alpar@cs.elte.hu}
    1.20 +\author[elte]{P{\'e}ter Madarasi}
    1.21 +\ead{madarasip@caesar.elte.hu}
    1.22 +\address[egres]{MTA-ELTE Egerv{\'a}ry Research Group, Budapest, Hungary.}
    1.23 +\address[elte]{Department of Operations Research, E{\"o}tv{\"o}s Lor{\'a}nd University, Budapest, Hungary.}
    1.24  
    1.25  \begin{abstract}
    1.26 -Subgraph isomorphism is a well-known NP-Complete problem, while its
    1.27 -special case, the graph isomorphism problem is one of the few problems
    1.28 -in NP neither known to be in P nor NP-Complete. Their appearance in
    1.29 -many fields of application such as pattern analysis, computer vision
    1.30 -questions and the analysis of chemical and biological systems has
    1.31 -fostered the design of various algorithms for handling special graph
    1.32 -structures.
    1.33  
    1.34 -This paper presents VF2++, a new algorithm based on the original VF2,
    1.35 -which runs significantly faster on most test cases and performs
    1.36 -especially well on special graph classes stemming from biological
    1.37 -questions. VF2++ handles graphs of thousands of nodes in practically
    1.38 -near linear time including preprocessing. Not only is it an improved
    1.39 -version of VF2, but in fact, it is by far the fastest existing
    1.40 -algorithm especially on biological graphs.
    1.41 +  This paper presents a largely improved version of the VF2 algorithm
    1.42 +  for the \emph{Subgraph Isomorphism Problem}. The improvements are
    1.43 +  twofold. Firstly, it is based on a new approach for determining the
    1.44 +  matching order of the nodes, and secondly, more efficient -
    1.45 +  nevertheless easier to compute - cutting rules significantly
    1.46 +  reducing the search space are applied.
    1.47  
    1.48 -The reason for VF2++' superiority over VF2 is twofold. Firstly, taking
    1.49 -into account the structure and the node labeling of the graph, VF2++
    1.50 -determines a state order in which most of the unfruitful branches of
    1.51 -the search space can be pruned immediately. Secondly, introducing more
    1.52 -efficient - nevertheless still easier to compute - cutting rules
    1.53 -reduces the chance of going astray even further.
    1.54 +  In addition to the usual subgraph isomorphism, the paper also
    1.55 +  presents specialized versions for the \emph{Induced Subgraph
    1.56 +    Isomorphism} and for the \emph{Graph Isomorphism Problems}.
    1.57  
    1.58 -In addition to the usual subgraph isomorphism, specialized versions
    1.59 -for induced subgraph isomorphism and for graph isomorphism are
    1.60 -presented. VF2++ has gained a runtime improvement of one order of
    1.61 -magnitude respecting induced subgraph isomorphism and a better
    1.62 -asymptotical behaviour in the case of graph isomorphism problem.
    1.63 -
    1.64 -After having provided the description of VF2++, in order to evaluate
    1.65 -its effectiveness, an extensive comparison to the contemporary other
    1.66 -algorithms is shown, using a wide range of inputs, including both real
    1.67 -life biological and chemical datasets and standard randomly generated
    1.68 -graph series.
    1.69 -
    1.70 -The work was motivated and sponsored by QuantumBio Inc., and all the
    1.71 -developed algorithms are available as the part of the open source
    1.72 -LEMON graph and network optimization library
    1.73 -(http://lemon.cs.elte.hu).
    1.74 +  Finally, an extensive experimental evaluation is provided using a
    1.75 +  wide range of inputs, including both real life biological and
    1.76 +  chemical datasets and standard randomly generated graph series. The
    1.77 +  results show major and consistent running time improvements over the
    1.78 +  other known methods.
    1.79 + 
    1.80 +  The C++ implementations of the algorithms are available open source as
    1.81 +  the part of the LEMON graph and network optimization library.
    1.82 +  
    1.83  \end{abstract}
    1.84  
    1.85  \begin{keyword}
    1.86 +  Computational Biology, Subgraph Isomorphism Problem
    1.87  %% keywords here, in the form: keyword \sep keyword
    1.88  
    1.89  %% PACS codes here, in the form: \PACS code \sep code