P 
Doerfel, S. & Jäschke, R.
(2013):
An analysis of tagrecommender evaluation procedures.
In: Proceedings of the 7th ACM conference on Recommender systems,
New York, NY, USA.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
Since the rise of collaborative tagging systems on the web, the tag recommendation task  suggesting suitable tags to users of such systems while they add resources to their collection  has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used socalled postcores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a largescale experiment in which we analyze different tag recommendation algorithms on different cores of three realworld datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
@inproceedings{doerfel2013analysis,
author = {Doerfel, Stephan and Jäschke, Robert},
title = {An analysis of tagrecommender evaluation procedures},
booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
series = {RecSys '13},
publisher = {ACM},
address = {New York, NY, USA},
year = {2013},
pages = {343346},
url = {https://www.kde.cs.unikassel.de/pub/pdf/doerfel2013analysis.pdf},
doi = {10.1145/2507157.2507222},
isbn = {9781450324090},
keywords = {2013, bibsonomy, bookmarking, collaborative, core, evaluation, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging},
abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task  suggesting suitable tags to users of such systems while they add resources to their collection  has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used socalled postcores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a largescale experiment in which we analyze different tag recommendation algorithms on different cores of three realworld datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.}
}
%0 = inproceedings
%A = Doerfel, Stephan and Jäschke, Robert
%B = Proceedings of the 7th ACM conference on Recommender systems
%C = New York, NY, USA
%D = 2013
%I = ACM
%T = An analysis of tagrecommender evaluation procedures
%U = https://www.kde.cs.unikassel.de/pub/pdf/doerfel2013analysis.pdf

J 
Heidtmann, K.
(2013):
InternetGraphen.
In: InformatikSpektrum,
Ausgabe/Number: 5,
Vol. 36,
Verlag/Publisher: Springer Berlin Heidelberg.
Erscheinungsjahr/Year: 2013.
Seiten/Pages: 440448.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale OnlineNetze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet.
@article{noKey,
author = {Heidtmann, Klaus},
title = {InternetGraphen},
journal = {InformatikSpektrum},
publisher = {Springer Berlin Heidelberg},
year = {2013},
volume = {36},
number = {5},
pages = {440448},
url = {http://dx.doi.org/10.1007/s002870120654z},
doi = {10.1007/s002870120654z},
issn = {01706012},
keywords = {Graph, Graphen, Informatik, InformatikSpektrum, Internet, Spektrum, graphs},
abstract = {Bildeten die Keimzellen des Internet noch kleine und einfach strukturierte Netze, so vergrößerten sich sowohl seine physikalischen als auch seine logischen Topologien später rasant. Wuchs einerseits das Netz aus Rechnern als Knoten und Verbindungsleitungen als Kanten immer weiter, so bedienten sich andererseits gleichzeitig immer mehr Anwendungen dieser Infrastruktur, um darüber ihrerseits immer größere und komplexere virtuelle Netze zu weben, z. B. das WWW oder soziale OnlineNetze. Auf jeder Ebene dieser Hierarchie lassen sich die jeweiligen Netztopologien mithilfe von Graphen beschreiben und so mathematisch untersuchen. So ergeben sich interessante Einblicke in die Struktureigenschaften unterschiedlicher Graphentypen, die großen Einfluss auf die Leistungsfähigkeit des Internet haben. Hierzu werden charakteristische Eigenschaften und entsprechende Kenngrößen verschiedener Graphentypen betrachtet wie der Knotengrad, die Durchschnittsdistanz, die Variation der Kantendichte in unterschiedlichen Netzteilen und die topologische Robustheit als Widerstandsfähigkeit gegenüber Ausfällen und Angriffen. Es wird dabei Bezug genommen auf analytische, simulative und zahlreiche empirische Untersuchungen des Internets und hingewiesen auf Simulationsprogramme sowie Abbildungen von Internetgraphen im Internet. }
}
%0 = article
%A = Heidtmann, Klaus
%D = 2013
%I = Springer Berlin Heidelberg
%T = InternetGraphen
%U = http://dx.doi.org/10.1007/s002870120654z

J 
Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N.
(2013):
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations.
In: cs.IR,
Vol. 1310.1498,
Erscheinungsjahr/Year: 2013.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an indepth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graphrepresentation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
@article{landia2013deeper,
author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
journal = {cs.IR},
year = {2013},
volume = {1310.1498},
url = {http://arxiv.org/abs/1310.1498},
keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging},
abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an indepth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graphrepresentation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}
}
%0 = article
%A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan
%D = 2013
%T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations
%U = http://arxiv.org/abs/1310.1498

Ghosh, R. & Lerman, K.
(2009):
Structure of Heterogeneous Networks.
[Volltext] [Kurzfassung] [BibTeX] [Endnote] Heterogeneous networks play a key role in the evolution of communities and e decisions individuals make. These networks link different types of tities, for example, people and the events they attend. Network analysis gorithms usually project such networks unto simple graphs composed of tities of a single type. In the process, they conflate relations between tities of different types and loose important structural information. We velop a mathematical framework that can be used to compactly represent and alyze heterogeneous networks that combine multiple entity and link types. We neralize Bonacich centrality, which measures connectivity between nodes by e number of paths between them, to heterogeneous networks and use this asure to study network structure. Specifically, we extend the popular dularitymaximization method for community detection to use this centrality tric. We also rank nodes based on their connectivity to other nodes. One vantage of this centrality metric is that it has a tunable parameter we can e to set the length scale of interactions. By studying how rankings change th this parameter allows us to identify important nodes in the network. We ply the proposed method to analyze the structure of several heterogeneous tworks. We show that exploiting additional sources of evidence corresponding links between, as well as among, different entity types yields new insights to network structure.
@misc{Ghosh2009,
author = {Ghosh, Rumi and Lerman, Kristina},
title = {Structure of Heterogeneous Networks},
year = {2009},
note = {cite arxiv:0906.2212
}, url = {http://arxiv.org/abs/0906.2212}, keywords = {graph, graphs, heterogenous, measures, multimode, networks, sna}, abstract = { Heterogeneous networks play a key role in the evolution of communities andthe decisions individuals make. These networks link different types ofentities, for example, people and the events they attend. Network analysisalgorithms usually project such networks unto simple graphs composed ofentities of a single type. In the process, they conflate relations betweenentities of different types and loose important structural information. Wedevelop a mathematical framework that can be used to compactly represent andanalyze heterogeneous networks that combine multiple entity and link types. Wegeneralize Bonacich centrality, which measures connectivity between nodes bythe number of paths between them, to heterogeneous networks and use thismeasure to study network structure. Specifically, we extend the popularmodularitymaximization method for community detection to use this centralitymetric. We also rank nodes based on their connectivity to other nodes. Oneadvantage of this centrality metric is that it has a tunable parameter we canuse to set the length scale of interactions. By studying how rankings changewith this parameter allows us to identify important nodes in the network. Weapply the proposed method to analyze the structure of several heterogeneousnetworks. We show that exploiting additional sources of evidence correspondingto links between, as well as among, different entity types yields new insightsinto network structure.} }
%0 = misc
%A = Ghosh, Rumi and Lerman, Kristina
%B = }
%C =
%D = 2009
%I =
%T = Structure of Heterogeneous Networks}
%U = http://arxiv.org/abs/0906.2212


Noack, A.
(2008):
Modularity clustering is forcedirected layout.
[Volltext] [Kurzfassung] [BibTeX] [Endnote] Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.
@misc{noack08modularity,
author = {Noack, Andreas},
title = {Modularity clustering is forcedirected layout},
year = {2008},
url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052},
keywords = {clustering, communities, community, graph, layout, modularity, network, sna},
abstract = { Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.}
}
%0 = misc
%A = Noack, Andreas
%B = }
%C =
%D = 2008
%I =
%T = Modularity clustering is forcedirected layout}
%U = http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052


P 
Zhu, F.; Chen, C.; Yan, X.; Han, J. & Yu, P. S.
(2008):
Graph OLAP: Towards Online Analytical Processing on Graphs.
In: Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.,
[BibTeX][Endnote]
@inproceedings{zhu2008graph,
author = {Zhu, Feida and Chen, Chen and Yan, Xifeng and Han, Jiawei and Yu, Philip S},
title = {Graph OLAP: Towards Online Analytical Processing on Graphs},
booktitle = {Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.},
year = {2008},
keywords = {graph, graphs, olap, sna}
}
%0 = inproceedings
%A = Zhu, Feida and Chen, Chen and Yan, Xifeng and Han, Jiawei and Yu, Philip S
%B = Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.
%D = 2008
%T = Graph OLAP: Towards Online Analytical Processing on Graphs

P 
Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G.
(2006):
Information Retrieval in Folksonomies: Search and Ranking.
In: Proceedings of the 3rd European Semantic Web Conference,
[BibTeX][Endnote]
@inproceedings{hotho2006information,
author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
title = {Information Retrieval in Folksonomies: Search and Ranking},
booktitle = {Proceedings of the 3rd European Semantic Web Conference},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {2006},
pages = {411426},
keywords = {FCA, OntologyHandbook, folkrank, folksonomy, graph, information, mining, pagerank, rank, ranking, retrieval, search, seminar2006}
}
%0 = inproceedings
%A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd
%B = Proceedings of the 3rd European Semantic Web Conference
%D = 2006
%I = Springer
%T = Information Retrieval in Folksonomies: Search and Ranking

P 
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.
(2006):
Content Aggregation on Knowledge Bases using Graph Clustering.
In: The Semantic Web: Research and Applications,
Heidelberg.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peertopeer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on
@inproceedings{schmitz2006content,
author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
title = {Content Aggregation on Knowledge Bases using Graph Clustering},
editor = {Sure, York and Domingue, John},
booktitle = {The Semantic Web: Research and Applications},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2006},
volume = {4011},
pages = {530544},
url = {http://www.kde.cs.unikassel.de/stumme/papers/2006/schmitz2006content.pdf},
keywords = {2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory},
abstract = {Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at establishing peertopeer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper provides a graph clustering technique on
%0 = inproceedings
%A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd
%B = The Semantic Web: Research and Applications
%C = Heidelberg
%D = 2006
%I = Springer
%T = Content Aggregation on Knowledge Bases using Graph Clustering
%U = http://www.kde.cs.unikassel.de/stumme/papers/2006/schmitz2006content.pdf

P 
Brandes, U. & Willhalm, T.
(2002):
Visualization of bibliographic networks with a reshaped landscape metaphor.
In: Proceedings of the symposium on Data Visualisation 2002,
AirelaVille, Switzerland, Switzerland.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.
@inproceedings{Brandes:2002:VBN:509740.509765,
author = {Brandes, U. and Willhalm, T.},
title = {Visualization of bibliographic networks with a reshaped landscape metaphor},
booktitle = {Proceedings of the symposium on Data Visualisation 2002},
series = {VISSYM '02},
publisher = {Eurographics Association},
address = {AirelaVille, Switzerland, Switzerland},
year = {2002},
pages = {159ff},
url = {http://portal.acm.org/citation.cfm?id=509740.509765},
isbn = {158113536X},
keywords = {bibliographic, bibliography, citation, graph, networks, sna},
abstract = {We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape's surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on data from the graph drawing literature.}
}
%0 = inproceedings
%A = Brandes, U. and Willhalm, T.
%B = Proceedings of the symposium on Data Visualisation 2002
%C = AirelaVille, Switzerland, Switzerland
%D = 2002
%I = Eurographics Association
%T = Visualization of bibliographic networks with a reshaped landscape metaphor
%U = http://portal.acm.org/citation.cfm?id=509740.509765

P 
Eklund, P.; Groh, B.; Stumme, G. & Wille, R.
(2000):
ContextualLogic Extension of TOSCANA..
In: Conceptual Structures: Logical, Linguistic, and Computational,
Heidelberg.
[Volltext]
[BibTeX][Endnote]
@inproceedings{eklund00contextual,
author = {Eklund, P. and Groh, B. and Stumme, G. and Wille, R.},
title = {ContextualLogic Extension of TOSCANA.},
editor = {Ganter, B. and Mineau, G. W.},
booktitle = {Conceptual Structures: Logical, Linguistic, and Computational},
series = {LNAI},
publisher = {Springer},
address = {Heidelberg},
year = {2000},
volume = {1867},
pages = {453467},
url = {http://www.kde.cs.unikassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf},
keywords = {2000, analysis, cg, cgs, concept, conceptual, fca, formal, graph, graphs, iccs, myown, toscana}
}
%0 = inproceedings
%A = Eklund, P. and Groh, B. and Stumme, G. and Wille, R.
%B = Conceptual Structures: Logical, Linguistic, and Computational
%C = Heidelberg
%D = 2000
%I = Springer
%T = ContextualLogic Extension of TOSCANA.
%U = http://www.kde.cs.unikassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf

P 
Prediger, S. & Wille, R.
(1999):
The Lattice of Concept Graphs of a Relationally Scaled Context.
In: ICCS,
[Volltext]
[BibTeX][Endnote]
@inproceedings{prediger99lattice,
author = {Prediger, Susanne and Wille, Rudolf},
title = {The Lattice of Concept Graphs of a Relationally Scaled Context},
editor = {Tepfenhart, William M. and Cyre, Walling R.},
booktitle = {ICCS},
series = {Lecture Notes in Computer Science},
publisher = {Springer},
year = {1999},
volume = {1640},
pages = {401414},
url = {http://dblp.unitrier.de/db/conf/iccs/iccs99.html#PredigerW99},
isbn = {3540662235},
keywords = {analysis, cg, concept, fca, formal, graph, graphs}
}
%0 = inproceedings
%A = Prediger, Susanne and Wille, Rudolf
%B = ICCS
%D = 1999
%I = Springer
%T = The Lattice of Concept Graphs of a Relationally Scaled Context
%U = http://dblp.unitrier.de/db/conf/iccs/iccs99.html#PredigerW99

P 
Wille, R.
(1997):
Conceptual Graphs and Formal Concept Analysis.
In: Conceptual Structures: Fulfilling Peirce's Dream,
Heidelberg.
[BibTeX][Endnote]
@inproceedings{wille97conceptual,
author = {Wille, Rudolf},
title = {Conceptual Graphs and Formal Concept Analysis},
editor = {Lukose, D. and Delugach, H. and Keeler, M. and Searle, L. and Sowa, J. F.},
booktitle = {Conceptual Structures: Fulfilling Peirce's Dream},
series = {Lecture Notes in Artificial Intelligence},
publisher = {Springer},
address = {Heidelberg},
year = {1997},
volume = {1257},
pages = {290303},
keywords = {ag1, analysis, begriffsanalyse, cg, concept, conceptual, darmstadt, fba, fca, formal, graph, graphs}
}
%0 = inproceedings
%A = Wille, Rudolf
%B = Conceptual Structures: Fulfilling Peirce's Dream
%C = Heidelberg
%D = 1997
%I = Springer
%T = Conceptual Graphs and Formal Concept Analysis

P 
Stumme, G. & Wille, R.
(1995):
A Geometrical Heuristic for Drawing Concept Lattices.
In: Graph Drawing,
Heidelberg.
[Volltext]
[BibTeX][Endnote]
@inproceedings{stumme95geometrical,
author = {Stumme, Gerd and Wille, Rudolf},
title = {A Geometrical Heuristic for Drawing Concept Lattices},
editor = {Tamassia, R. and Tollis, I.G.},
booktitle = {Graph Drawing},
series = {LNCS},
publisher = {Springer},
address = {Heidelberg},
year = {1995},
volume = {894},
pages = {452459},
url = {http://www.kde.cs.unikassel.de/stumme/papers/1994/P1677GD94.pdf},
keywords = {1995, analysis, concept, drawing, fca, formal, graph, lattices, myown}
}
%0 = inproceedings
%A = Stumme, Gerd and Wille, Rudolf
%B = Graph Drawing
%C = Heidelberg
%D = 1995
%I = Springer
%T = A Geometrical Heuristic for Drawing Concept Lattices
%U = http://www.kde.cs.unikassel.de/stumme/papers/1994/P1677GD94.pdf
