BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
6,"978-1-4503-2409-0","doerfel2013analysis","Doerfel, Stephan & Jäschke, Robert","An analysis of tag-recommender evaluation procedures","",,,"","343--346",2013,"New York, NY, USA","","https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf","Proceedings of the 7th ACM conference on Recommender systems","","","RecSys '13","","ACM","","","","","","","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.","","2013, bibsonomy, bookmarking, collaborative, core, evaluation, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging","",""
7,"","noKey","Heidtmann, Klaus","Internet-Graphen","Informatik-Spektrum",36,5,"","440-448",2013,"","","http://dx.doi.org/10.1007/s00287-012-0654-z","","","","","","Springer Berlin Heidelberg","","","","","","","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. ","Internet-Graphen - Springer","Graph, Graphen, Informatik, Informatik-Spektrum, Internet, Spektrum, graphs","",""
7,"","landia2013deeper","Landia, Nikolas; Doerfel, Stephan; Jäschke, Robert; Anand, Sarabjot Singh; Hotho, Andreas & Griffiths, Nathan","Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations","cs.IR",1310.1498,,"","",2013,"","","http://arxiv.org/abs/1310.1498","","","","","","","","","","","","","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.","","2013, bookmarking, collaborative, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging","",""
10,"","Ghosh2009","Ghosh, Rumi & Lerman, Kristina","Structure of Heterogeneous Networks","",,,"","",2009,"","cite arxiv:0906.2212
","http://arxiv.org/abs/0906.2212","","","","","","","","","","","",""," 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 popularmodularity-maximization 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.","[0906.2212] Structure of Heterogeneous Networks","graph, graphs, heterogenous, measures, multi-mode, networks, sna","",""
10,"","noack08modularity","Noack, Andreas","Modularity clustering is force-directed layout","",,,"","",2008,"","","http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052","","","","","","","","","","","",""," 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.","[0807.4052] Modularity clustering is force-directed layout","clustering, communities, community, graph, layout, modularity, network, sna","",""
6,"","zhu2008graph","Zhu, Feida; Chen, Chen; Yan, Xifeng; Han, Jiawei & Yu, Philip S","Graph OLAP: Towards Online Analytical Processing on Graphs","",,,"December","",2008,"","","","Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008.","","","","","","","","","","","","","Resource: Graph OLAP: Towards Online Analytical Processing on Graphs","graph, graphs, olap, sna","",""
6,"","hotho2006information","Hotho, Andreas; J{\"a}schke, Robert; Schmitz, Christoph & Stumme, Gerd","Information Retrieval in Folksonomies: Search and Ranking","",,,"","411-426",2006,"","","","Proceedings of the 3rd European Semantic Web Conference","","","Lecture Notes in Computer Science","","Springer","","","","","","","","","FCA, OntologyHandbook, folkrank, folksonomy, graph, information, mining, pagerank, rank, ranking, retrieval, search, seminar2006","",""
6,"","schmitz2006content","Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd","Content Aggregation on Knowledge Bases using Graph Clustering","",4011,,"","530-544",2006,"Heidelberg","","http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf","The Semantic Web: Research and Applications","","","LNAI","Sure, York & Domingue, John","Springer","","","","","","","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.","","2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory","",""
6,"1-58113-536-X","Brandes:2002:VBN:509740.509765","Brandes, U. & Willhalm, T.","Visualization of bibliographic networks with a reshaped landscape metaphor","",,,"","159--ff",2002,"Aire-la-Ville, Switzerland, Switzerland","","http://portal.acm.org/citation.cfm?id=509740.509765","Proceedings of the symposium on Data Visualisation 2002","","","VISSYM '02","","Eurographics Association","","","","","","","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.","Visualization of bibliographic networks with a reshaped landscape metaphor","bibliographic, bibliography, citation, graph, networks, sna","",""
6,"","eklund00contextual","Eklund, P.; Groh, B.; Stumme, G. & Wille, R.","Contextual-Logic Extension of TOSCANA.","",1867,,"","453-467",2000,"Heidelberg","","http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf","Conceptual Structures: Logical, Linguistic, and Computational","","","LNAI","Ganter, B. & Mineau, G. W.","Springer","","","","","","","","Publications of Gerd Stumme","2000, analysis, cg, cgs, concept, conceptual, fca, formal, graph, graphs, iccs, myown, toscana","",""
6,"3-540-66223-5","prediger99lattice","Prediger, Susanne & Wille, Rudolf","The Lattice of Concept Graphs of a Relationally Scaled Context","",1640,,"","401-414",1999,"","","http://dblp.uni-trier.de/db/conf/iccs/iccs99.html#PredigerW99","ICCS","","","Lecture Notes in Computer Science","Tepfenhart, William M. & Cyre, Walling R.","Springer","","","","","","","","","analysis, cg, concept, fca, formal, graph, graphs","",""
6,"","wille97conceptual","Wille, Rudolf","Conceptual Graphs and Formal Concept Analysis","",1257,,"","290--303",1997,"Heidelberg","","","Conceptual Structures: Fulfilling Peirce's Dream","","","Lecture Notes in Artificial Intelligence","Lukose, D.; Delugach, H.; Keeler, M.; Searle, L. & Sowa, J. F.","Springer","","","","","","","","","ag1, analysis, begriffsanalyse, cg, concept, conceptual, darmstadt, fba, fca, formal, graph, graphs","",""
6,"","stumme95geometrical","Stumme, Gerd & Wille, Rudolf","A Geometrical Heuristic for Drawing Concept Lattices","",894,,"","452-459",1995,"Heidelberg","","http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf","Graph Drawing","","","LNCS","Tamassia, R. & Tollis, I.G.","Springer","","","","","","","","Publications of Gerd Stumme","1995, analysis, concept, drawing, fca, formal, graph, lattices, myown","",""