%0 
%0 Journal Article
%A Heidtmann, Klaus
%D 2013
%T Internet-Graphen
%E 
%B Informatik-Spektrum
%C 
%I Springer Berlin Heidelberg
%V 36
%6
%N 5
%P 440-448
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%@ 0170-6012
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%2 Internet-Graphen - Springer
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%F noKey
%K Graph, Graphen, Informatik, Informatik-Spektrum, Internet, Spektrum, graphs
%X 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 Online-Netze. 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. 
%Z 
%U http://dx.doi.org/10.1007/s00287-012-0654-z
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%^

%0 
%0 Journal Article
%A Mucha, Peter J.; Richardson, Thomas; Macon, Kevin; Porter, Mason A. & Onnela, Jukka-Pekka
%D 2010
%T Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
%E 
%B Science
%C 
%I 
%V 328
%6
%N 5980
%P 876-878
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%2 Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
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%F Mucha14052010
%K communities, community, evolving, graphs, networks, time
%X Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.
%Z 
%U http://www.sciencemag.org/content/328/5980/876.abstract
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%^

%0 
%0 Generic
%A Ghosh, Rumi & Lerman, Kristina
%D 2009
%T Structure of Heterogeneous Networks
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%2 [0906.2212] Structure of Heterogeneous Networks
%3 misc
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%$ 
%F Ghosh2009
%K graph, graphs, heterogenous, measures, multi-mode, networks, sna
%X   Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure.

%Z cite arxiv:0906.2212

%U http://arxiv.org/abs/0906.2212
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%0 
%0 Conference Proceedings
%A Zhu, Feida; Chen, Chen; Yan, Xifeng; Han, Jiawei & Yu, Philip S
%D 2008
%T Graph OLAP: Towards Online Analytical Processing on Graphs
%E 
%B Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.
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%2 Resource: Graph OLAP: Towards Online Analytical Processing on Graphs
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%F zhu2008graph
%K graph, graphs, olap, sna
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%0 
%0 Conference Proceedings
%A 
%D 2005
%T Conceptual Structures: Common Semantics for Sharing Knowledge, 13th International Conference on Conceptual Structures, ICCS 2005, Kassel, Germany, July 17-22, 2005, Proceedings
%E Dau, Frithjof; Mugnier, Marie-Laure & Stumme, Gerd
%B ICCS
%C 
%I Springer
%V 3596
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%S Lecture Notes in Computer Science
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%2 Publications of Gerd Stumme
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%F conf/iccs/2005
%K 2005, Germany, Kassel, analysis, concept, conceptual, conference, fca, formal, graphs, iccs, knowledge, l3s, myown, proceedings, sharing, structures
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%Z 
%U http://www.kde.cs.uni-kassel.de/conf/iccs05
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%0 
%0 Conference Proceedings
%A 
%D 2005
%T Contributions to ICCS 2005
%E Dau, Frithjof; Mugnier, Marie-Laure & Stumme, Gerd
%B Contributions to ICCS 2005
%C Kassel
%I kassel university press
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%F dau05contributions
%K 2005, Germany, Kassel, analysis, concept, conceptual, conference, fca, formal, graphs, iccs, knowledge, l3s, myown, proceedings, sharing, structures
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%Z 
%U http://www.kde.cs.uni-kassel.de/conf/iccs05
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%0 
%0 Conference Proceedings
%A 
%D 2001
%T Conceptual Structures -- Broadening the Base. Proc. 9th International Conference on Conceptual Structures
%E Delugach, H. & Stumme, G.
%B 
%C Heidelberg
%I Springer
%V 2120
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%N 
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%K 2001, analysis, cg, cgs, concept, conceptual, fca, formal, graphs, iccs, myown, structures
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%0 
%0 Conference Proceedings
%A Eklund, P.; Groh, B.; Stumme, G. & Wille, R.
%D 2000
%T Contextual-Logic Extension of TOSCANA.
%E Ganter, B. & Mineau, G. W.
%B Conceptual Structures: Logical, Linguistic, and Computational
%C Heidelberg
%I Springer
%V 1867
%6
%N 
%P 453-467
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%F eklund00contextual
%K 2000, analysis, cg, cgs, concept, conceptual, fca, formal, graph, graphs, iccs, myown, toscana
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf
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%0 Journal Article
%A Stumme, G.
%D 2000
%T 8th International Conference on Conceptual Structures. Conference Report
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%B Knowledge Organization
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%V 27
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%N 3
%P 162
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%K 2000, analysis, cg, concept, conceptual, conference, fcacgs, formal, graphs, iccs, lattices, myown, report, structures
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ConferenceReportICCS00.pdf
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%0 
%0 Conference Proceedings
%A 
%D 2000
%T Working with Conceptual Structures -- Contributions to ICCS 2000. Suppl. Proc. 8th International
                  Conference on Conceptual Structures (ICCS 2000)
%E Stumme, G.
%B 
%C Aachen
%I Shaker
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%K 2000, analysis, cg, cgs, concept, conceptual, conference, fca, formal, graphs, iccs, myown, proceedings, structures
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%0 
%0 Conference Proceedings
%A Mineau, Guy; Stumme, Gerd & Wille, Rudolf
%D 1999
%T Conceptual Structures  Represented by Conceptual Graphs and Formal Concept Analysis
%E Tepfenhart, W. & Cyre, W.
%B Conceptual Structures: Standards  and  Practices. Proc. ICCS '99
%C Heidelberg
%I Springer
%V 1640
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%P 423-441
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%K 1999, analysis, cg, cgs, concept, conceptual, fca, formal, graphs, iccs, knowledge, myown, representation, structures
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%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/1999/ICCS99.pdf
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%0 
%0 Conference Proceedings
%A Prediger, Susanne & Wille, Rudolf
%D 1999
%T The Lattice of Concept Graphs of a Relationally Scaled Context
%E Tepfenhart, William M. & Cyre, Walling R.
%B ICCS
%C 
%I Springer
%V 1640
%6
%N 
%P 401-414
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%S Lecture Notes in Computer Science
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%4 conf/iccs/1999
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%F prediger99lattice
%K analysis, cg, concept, fca, formal, graph, graphs
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%Z 
%U http://dblp.uni-trier.de/db/conf/iccs/iccs99.html#PredigerW99
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%0 
%0 Conference Proceedings
%A Wille, Rudolf
%D 1997
%T Conceptual Graphs and Formal Concept Analysis
%E Lukose, D.; Delugach, H.; Keeler, M.; Searle, L. & Sowa, J. F.
%B Conceptual Structures: Fulfilling Peirce's Dream
%C Heidelberg
%I Springer
%V 1257
%6
%N 
%P 290--303
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%S Lecture Notes in Artificial Intelligence
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%K ag1, analysis, begriffsanalyse, cg, concept, conceptual, darmstadt, fba, fca, formal, graph, graphs
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%0 
%0 Book
%A Sowa, J. F.
%D 1984
%T Conceptual Structures: Information Processing in Mind and Machine
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%B 
%C Reading, MA
%I Addison-Wesley Publishing Company
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%K cg, cgs, conceptual, graphs, information, structures
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