@article{noKey,
  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 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. },
  author = {Heidtmann, Klaus},
  doi = {10.1007/s00287-012-0654-z},
  interhash = {d1ab5ecb2270150ba3d46e156d3a1139},
  intrahash = {783481b980117c756d4560c1640ae4a0},
  issn = {0170-6012},
  journal = {Informatik-Spektrum},
  language = {German},
  number = 5,
  pages = {440-448},
  publisher = {Springer Berlin Heidelberg},
  title = {Internet-Graphen},
  url = {http://dx.doi.org/10.1007/s00287-012-0654-z},
  volume = 36,
  year = 2013
}

@article{landia2013deeper,
  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 in-depth 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 graph-representation 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.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{doerfel2013analysis,
  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 so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.},
  acmid = {2507222},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert},
  booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
  doi = {10.1145/2507157.2507222},
  interhash = {3eaf2beb1cdad39b7c5735a82c3338dd},
  intrahash = {aa4b3d79a362d7415aaa77625b590dfa},
  isbn = {978-1-4503-2409-0},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {343--346},
  publisher = {ACM},
  series = {RecSys '13},
  title = {An analysis of tag-recommender evaluation procedures},
  url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
  year = 2013
}

@inproceedings{Brandes:2002:VBN:509740.509765,
  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.},
  acmid = {509765},
  address = {Aire-la-Ville, Switzerland, Switzerland},
  author = {Brandes, U. and Willhalm, T.},
  booktitle = {Proceedings of the symposium on Data Visualisation 2002},
  interhash = {7d070baa654fc70cb8a0b1e373d90e2a},
  intrahash = {e5e72eed2d871523dc1100f060658a1c},
  isbn = {1-58113-536-X},
  location = {Barcelona, Spain},
  pages = {159--ff},
  publisher = {Eurographics Association},
  series = {VISSYM '02},
  title = {Visualization of bibliographic networks with a reshaped landscape metaphor},
  url = {http://portal.acm.org/citation.cfm?id=509740.509765},
  year = 2002
}

@misc{Ghosh2009,
  abstract = {  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.
},
  author = {Ghosh, Rumi and Lerman, Kristina},
  interhash = {761e199eb96643cf601e15cb03c3285a},
  intrahash = {3a4a889123d20e0a4d14d06f670de54b},
  note = {cite arxiv:0906.2212
},
  title = {Structure of Heterogeneous Networks},
  url = {http://arxiv.org/abs/0906.2212},
  year = 2009
}

@inproceedings{hotho2006information,
  author = {Hotho, Andreas and J{\"a}schke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  interhash = {882bd942131c6c303bdc9c4732287ae9},
  intrahash = {087d10ce5603cef6c6a8b443700368a2},
  pages = {411-426},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  year = 2006
}

@inproceedings{eklund00contextual,
  address = {Heidelberg},
  author = {Eklund, P. and Groh, B. and Stumme, G. and Wille, R.},
  booktitle = {Conceptual Structures: Logical, Linguistic, and Computational},
  comment = {alpha},
  editor = {Ganter, B. and Mineau, G. W.},
  interhash = {b15b76a5407efbdbb2a11b18a13febcb},
  intrahash = {eb6e678cba7f48ae8604d59542cd79c6},
  pages = {453-467},
  publisher = {Springer},
  series = {LNAI},
  title = {Contextual-Logic Extension of TOSCANA.},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf},
  volume = 1867,
  year = 2000
}

@inproceedings{stumme95geometrical,
  address = {Heidelberg},
  author = {Stumme, Gerd and Wille, Rudolf},
  booktitle = {Graph Drawing},
  comment = {alpha},
  editor = {Tamassia, R. and Tollis, I.G.},
  interhash = {0e3ac1bae7ef38507b1ff5b5bc6c4d49},
  intrahash = {069db3a0aad592c82f35e1bbf701824f},
  pages = {452-459},
  publisher = {Springer},
  series = {LNCS},
  title = {A Geometrical Heuristic for Drawing Concept Lattices},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf},
  volume = 894,
  year = 1995
}

@inproceedings{zhu2008graph,
  author = {Zhu, Feida and Chen, Chen and Yan, Xifeng and Han, Jiawei and Yu, Philip S},
  booktitle = {Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.},
  interhash = {b9f7956dd1e140a386376df25f1a4117},
  intrahash = {066f245bd365c69acfff1378d72dc01e},
  month = {December},
  title = {{Graph OLAP: Towards Online Analytical Processing on Graphs}},
  year = 2008
}

@misc{noack08modularity,
  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.},
  author = {Noack, Andreas},
  interhash = {a2442ee608964a82be06224fd90d54d3},
  intrahash = {0186031133dc122ffd6ff33ded32c911},
  title = {Modularity clustering is force-directed layout},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052},
  year = 2008
}