%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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%S 
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%@ 0170-6012
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%2 Internet-Graphen - Springer
%3 article
%4 
%# 
%$ 
%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
%3 article
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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
%E
%B 
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%2 [0906.2212] Structure of Heterogeneous Networks
%3 misc
%4 
%# 
%$ 
%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
%+
%^

%0 
%0 Journal Article
%A Hanhijärvi, Sami; Garriga, Gemma & Puolamäki, Kai
%D 2009
%T Randomization techniques for graphs
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%2 Scientific Commons: Randomization techniques for graphs (2009), 2009 [Hanhijärvi, Sami, Garriga, Gemma, Puolamäki, Kai]
%3 article
%4 
%# 
%$ 
%F Hanhijärvi2009
%K graphs, randomization, toRead
%X Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statistical significance of data mining results. Given an input graph, our randomization method will sample data from the class of graphs that share certain structural properties with the input graph. Here we describe three alternative algorithms based on local edge swapping and Metropolis sampling. We test our framework with various graph data sets and mining algorithms for two applications, namely graph clustering and frequent subgraph mining.
%Z 
%U http://eprints.pascal-network.org/archive/00004486/
%+
%^

%0 
%0 Conference Proceedings
%A Mirowski, Piotr W. & LeCun, Yann
%D 2009
%T Dynamic Factor Graphs for Time Series Modeling.
%E Buntine, Wray L.; Grobelnik, Marko; Mladenic, Dunja & Shawe-Taylor, John
%B ECML/PKDD (2)
%C 
%I Springer
%V 5782
%6
%N 
%P 128-143
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 978-3-642-04173-0
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%)
%*
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%1 
%2 
%3 inproceedings
%4 conf/pkdd/2009-2
%# 
%$ 
%F conf/pkdd/MirowskiL09
%K 2009, ecml, factor, graphs, pkdd, series, time
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2009-2.html#MirowskiL09
%+
%^

%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.
%C 
%I 
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%8 December
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%2 Resource: Graph OLAP: Towards Online Analytical Processing on Graphs
%3 inproceedings
%4 
%# 
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%F zhu2008graph
%K graph, graphs, olap, sna
%X 
%Z 
%U 
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%^

%0 
%0 Journal Article
%A Baeza-Yates, Ricardo
%D 2007
%T Graphs from Search Engine Queries
%E 
%B SOFSEM 2007: Theory and Practice of Computer Science
%C 
%I 
%V 4362
%6
%N 
%P 1--8
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%2 SpringerLink - Book Chapter
%3 article
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%F baezayates2007graphs
%K sofsem2007, baeza_yates, query_log_mining, graphs
%X Server logs of search engines store traces of queries submitted by users, which include queries themselves along with Webpages selected in their answers. Here we describe several graph-based relations among queries and many applications wherethese graphs could be used.
%Z 
%U http://dx.doi.org/10.1007/978-3-540-69507-3_1
%+
%^

%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
%6
%N 
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%S Lecture Notes in Computer Science
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%2 Publications of Gerd Stumme
%3 proceedings
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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
%X 
%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
%V 
%6
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%2 Publications of Gerd Stumme
%3 proceedings
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%$ 
%F dau05contributions
%K 2005, Germany, Kassel, analysis, concept, conceptual, conference, fca, formal, graphs, iccs, knowledge, l3s, myown, proceedings, sharing, structures
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/conf/iccs05
%+
%^

%0 
%0 Journal Article
%A Blondel, Vincent D.; Gajardo, Anah\'\i,; Heymans, Maureen; Senellart, Pierre & Dooren, Paul Van
%D 2004
%T A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching
%E 
%B SIAM Rev.
%C 
%I Society for Industrial and Applied Mathematics
%V 46
%6
%N 4
%P 647--666
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%@ 0036-1445
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%1 
%2 A Measure of Similarity between Graph Vertices
%3 article
%4 
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%$ 
%F blondel2004measure
%K detect, graphs, similarity, synonymy
%X We introduce a concept of {similarity} between vertices of directed graphs. Let GA and GB be two directed graphs with, respectively, nA and nB vertices. We define an nB times nA similarity matrix S whose real entry sij expresses how similar vertex j (in GA) is to vertex i (in GB): we say that sij is their similarity score. The similarity matrix can be obtained as the limit of the normalized even iterates of Sk+1 = BSkAT + BTSkA, where A and B are adjacency matrices of the graphs and S0 is a matrix whose entries are all equal to 1. In the special case where GA = GB = G, the matrix S is square and the score sij is the similarity score between the vertices i and j of G. We point out that Kleinberg's "hub and authority" method to identify web-pages relevant to a given query can be viewed as a special case of our definition in the case where one of the graphs has two vertices and a unique directed edge between them. In analogy to Kleinberg, we show that our similarity scores are given by the components of a dominant eigenvector of a nonnegative matrix. Potential applications of our similarity concept are numerous. We illustrate an application for the automatic extraction of synonyms in a monolingual dictionary.
%Z 
%U http://portal.acm.org/citation.cfm?id=1035533.1035557
%+
%^

%0 
%0 Journal Article
%A Newman, M. E. J.
%D 2004
%T Analysis of weighted networks
%E 
%B Phys. Rev. E
%C 
%I American Physical Society
%V 70
%6
%N 5
%P 056131
%& 
%Y 
%S 
%7
%8 November
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%3 article
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%F PhysRevE.70.056131
%K COMMUNE, community, detection, graphs, modularity, networks, weighted
%X 
%Z 
%U http://pre.aps.org/abstract/PRE/v70/i5/e056131
%+
%^

%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
%6
%N 
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%S LNAI
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%F delugach01conceptual
%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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%S LNAI
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%3 inproceedings
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%F eklund00contextual
%K 2000, analysis, cg, cgs, concept, conceptual, fca, formal, graph, graphs, iccs, myown, toscana
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf
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%0 
%0 Journal Article
%A Stumme, G.
%D 2000
%T 8th International Conference on Conceptual Structures. Conference Report
%E 
%B Knowledge Organization
%C 
%I 
%V 27
%6
%N 3
%P 162
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%F stumme008thinternational
%K 2000, analysis, cg, concept, conceptual, conference, fcacgs, formal, graphs, iccs, lattices, myown, report, structures
%X 
%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
%6
%N 
%P 423-441
%& 
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%S LNAI
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%F mineau99conceptual
%K 1999, analysis, cg, cgs, concept, conceptual, fca, formal, graphs, iccs, knowledge, myown, representation, structures
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/1999/ICCS99.pdf
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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
%& 
%Y 
%S Lecture Notes in Artificial Intelligence
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%F wille97conceptual
%K ag1, analysis, begriffsanalyse, cg, concept, conceptual, darmstadt, fba, fca, formal, graph, graphs
%X 
%Z 
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%^

%0 
%0 Journal Article
%A Chein, Michel & Mugnier, Marie-Laure
%D 1992
%T Conceptual graphs: fundamental notions
%E 
%B Revue d'Intelligence Artificielle
%C 
%I 
%V 6
%6
%N 
%P 365-406
%& 
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%F m1992conceptual
%K conceptual, fundamental, graphs, notions
%X Nous définissons précisément les notions de base du modèle des graphes conceptuels de Sowa [Sowa 84] et en étudions les propriétés. Nos résultats portent principalement sur la structure de la relation de spécialisation, la correspondance entre opérations de graphes et opérations logiques, et la complexité algorithmique de la mise en œuvre du modèle  
%Z 
%U 
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%0 
%0 Book
%A Sowa, J. F.
%D 1984
%T Conceptual Structures: Information Processing in Mind and Machine
%E 
%B 
%C Reading, MA
%I Addison-Wesley Publishing Company
%V 
%6
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%F sowa84
%K cg, cgs, conceptual, graphs, information, structures
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%0 
%0 Journal Article
%A Erd\Ho,s, Pal & R\'e,nyi, Alfr\'e,d
%D 1959
%T On Random Graphs
%E 
%B Publications Mathematicae
%C 
%I 
%V 6
%6
%N 
%P 290
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%F erdos1959
%K analysis, graphs, network, random
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