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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Doerfel, S. & Jäschke, R. An analysis of tag-recommender evaluation procedures 2013 Proceedings of the 7th ACM conference on Recommender systems, pp. 343-346  inproceedings DOI URL 
    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.
    BibTeX:
    @inproceedings{doerfel2013analysis,
      author = {Doerfel, Stephan and Jäschke, Robert},
      title = {An analysis of tag-recommender evaluation procedures},
      booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
      publisher = {ACM},
      year = {2013},
      pages = {343--346},
      url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf},
      doi = {http://dx.doi.org/10.1145/2507157.2507222}
    }
    
    Heidtmann, K. Internet-Graphen 2013 Informatik-Spektrum
    Vol. 36(5), pp. 440-448 
    article DOI URL 
    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.
    BibTeX:
    @article{noKey,
      author = {Heidtmann, Klaus},
      title = {Internet-Graphen},
      journal = {Informatik-Spektrum},
      publisher = {Springer Berlin Heidelberg},
      year = {2013},
      volume = {36},
      number = {5},
      pages = {440-448},
      url = {http://dx.doi.org/10.1007/s00287-012-0654-z},
      doi = {http://dx.doi.org/10.1007/s00287-012-0654-z}
    }
    
    Landia, N., Doerfel, S., Jäschke, R., Anand, S.S., Hotho, A. & Griffiths, N. Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations 2013 cs.IR
    Vol. 1310.1498 
    article URL 
    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.
    BibTeX:
    @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}
    }
    
    Ghosh, R. & Lerman, K. Structure of Heterogeneous Networks 2009   misc URL 
    Abstract: 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
    dularity-maximization 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.
    BibTeX:
    @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} }
    Noack, A. Modularity clustering is force-directed layout 2008   misc URL 
    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.
    BibTeX:
    @misc{noack08modularity,
      author = {Noack, Andreas},
      title = {Modularity clustering is force-directed layout},
      year = {2008},
      url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052}
    }
    
    Zhu, F., Chen, C., Yan, X., Han, J. & Yu, P.S. Graph OLAP: Towards Online Analytical Processing on Graphs 2008 Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.  inproceedings  
    BibTeX:
    @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}
    }
    
    Hotho, A., Jäschke, R., Schmitz, C. & Stumme, G. Information Retrieval in Folksonomies: Search and Ranking 2006 Proceedings of the 3rd European Semantic Web Conference, pp. 411-426  inproceedings  
    BibTeX:
    @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},
      publisher = {Springer},
      year = {2006},
      pages = {411-426}
    }
    
    Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Content Aggregation on Knowledge Bases using Graph Clustering 2006
    Vol. 4011The Semantic Web: Research and Applications, pp. 530-544 
    inproceedings URL 
    Abstract: Recently, research projects such as PADLR and SWAP
    have developed tools like Edutella or Bibster, which are targeted at
    establishing peer-to-peer 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
    knowledge bases for that purpose. Using this clustering, we can show
    that our strategy requires up to 58% fewer queries than the
    baselines to yield full recall in a bibliographic P2PKM scenario.

    BibTeX:
    @inproceedings{schmitz2006content,
      author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
      title = {Content Aggregation on Knowledge Bases using Graph Clustering},
      booktitle = {The Semantic Web: Research and Applications},
      publisher = {Springer},
      year = {2006},
      volume = {4011},
      pages = {530-544},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf}
    }
    
    Brandes, U. & Willhalm, T. Visualization of bibliographic networks with a reshaped landscape metaphor 2002 Proceedings of the symposium on Data Visualisation 2002, pp. 159-ff  inproceedings URL 
    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.
    BibTeX:
    @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},
      publisher = {Eurographics Association},
      year = {2002},
      pages = {159--ff},
      url = {http://portal.acm.org/citation.cfm?id=509740.509765}
    }
    
    Eklund, P., Groh, B., Stumme, G. & Wille, R. Contextual-Logic Extension of TOSCANA. 2000
    Vol. 1867Conceptual Structures: Logical, Linguistic, and Computational, pp. 453-467 
    inproceedings URL 
    BibTeX:
    @inproceedings{eklund00contextual,
      author = {Eklund, P. and Groh, B. and Stumme, G. and Wille, R.},
      title = {Contextual-Logic Extension of TOSCANA.},
      booktitle = {Conceptual Structures: Logical, Linguistic, and Computational},
      publisher = {Springer},
      year = {2000},
      volume = {1867},
      pages = {453-467},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf}
    }
    
    Prediger, S. & Wille, R. The Lattice of Concept Graphs of a Relationally Scaled Context 1999
    Vol. 1640ICCS, pp. 401-414 
    inproceedings URL 
    BibTeX:
    @inproceedings{prediger99lattice,
      author = {Prediger, Susanne and Wille, Rudolf},
      title = {The Lattice of Concept Graphs of a Relationally Scaled Context},
      booktitle = {ICCS},
      publisher = {Springer},
      year = {1999},
      volume = {1640},
      pages = {401-414},
      url = {http://dblp.uni-trier.de/db/conf/iccs/iccs99.html#PredigerW99}
    }
    
    Wille, R. Conceptual Graphs and Formal Concept Analysis 1997
    Vol. 1257Conceptual Structures: Fulfilling Peirce's Dream, pp. 290-303 
    inproceedings  
    BibTeX:
    @inproceedings{wille97conceptual,
      author = {Wille, Rudolf},
      title = {Conceptual Graphs and Formal Concept Analysis},
      booktitle = {Conceptual Structures: Fulfilling Peirce's Dream},
      publisher = {Springer},
      year = {1997},
      volume = {1257},
      pages = {290--303}
    }
    
    Stumme, G. & Wille, R. A Geometrical Heuristic for Drawing Concept Lattices 1995
    Vol. 894Graph Drawing, pp. 452-459 
    inproceedings URL 
    BibTeX:
    @inproceedings{stumme95geometrical,
      author = {Stumme, Gerd and Wille, Rudolf},
      title = {A Geometrical Heuristic for Drawing Concept Lattices},
      booktitle = {Graph Drawing},
      publisher = {Springer},
      year = {1995},
      volume = {894},
      pages = {452-459},
      url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf}
    }
    

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