TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An Analysis of Tag-Recommender Evaluation Procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2013/ M2 - VL - IS - SP - 343 EP - 346 UR - http://doi.acm.org/10.1145/2507157.2507222 M3 - 10.1145/2507157.2507222 KW - 2013 KW - BibSonomy KW - core KW - evaluation KW - iteg KW - itegpub KW - l3s KW - myown KW - recsys KW - tag L1 - SN - 978-1-4503-2409-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An analysis of tag-recommender evaluation procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2013/ M2 - VL - IS - SP - 343 EP - 346 UR - https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf M3 - 10.1145/2507157.2507222 KW - 2013 KW - bibsonomy KW - bookmarking KW - collaborative KW - core KW - evaluation KW - folkrank KW - folksonomy KW - graph KW - iteg KW - itegpub KW - l3s KW - recommender KW - social KW - tagging L1 - SN - 978-1-4503-2409-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An Analysis of Tag-Recommender Evaluation Procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2013/ M2 - VL - IS - SP - 343 EP - 346 UR - http://doi.acm.org/10.1145/2507157.2507222 M3 - 10.1145/2507157.2507222 KW - 2013 KW - BibSonomy KW - core KW - evaluation KW - myown KW - recsys KW - tag L1 - SN - 978-1-4503-2409-0 N1 - An analysis of tag-recommender evaluation procedures N1 - AB - 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. ER - TY - JOUR AU - Batagelj, Vladimir AU - Zaveršnik, Matjaž T1 - Fast algorithms for determining (generalized) core groups in social networks JO - Advances in Data Analysis and Classification PY - 2011/ VL - 5 IS - 2 SP - 129 EP - 145 UR - http://dx.doi.org/10.1007/s11634-010-0079-y M3 - 10.1007/s11634-010-0079-y KW - analysis KW - core KW - network KW - sna KW - social L1 - SN - N1 - N1 - AB - The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “ ER - TY - JOUR AU - Batagelj, Vladimir AU - Zaveršnik, Matjaž T1 - Fast algorithms for determining (generalized) core groups in social networks JO - Advances in Data Analysis and Classification PY - 2011/ VL - 5 IS - 2 SP - 129 EP - 145 UR - http://dx.doi.org/10.1007/s11634-010-0079-y M3 - 10.1007/s11634-010-0079-y KW - core KW - graph L1 - SN - N1 - Advances in Data Analysis and Classification, Volume 5, Number 2 - SpringerLink N1 - AB - The structure of a large network (graph) can often be revealed by partitioning it into smaller and possibly more dense sub-networks that are easier to handle. One of such decompositions is based on “ k -cores”, proposed in 1983 by Seidman. Together with connectivity components, cores are one among few concepts that provide efficient decompositions of large graphs and networks. In this paper we propose an efficient algorithm for determining the cores decomposition of a given network with complexity $$O(m)$$, where m is the number of lines (edges or arcs). In the second part of the paper the classical concept of k -core is generalized in a way that uses a vertex property function instead of degree of a vertex. For local monotone vertex property functions the corresponding generalized cores can be determined in $$O(motn))$$ time, where n is the number of vertices and Δ is the maximum degree. Finally the proposed algorithms are illustrated by the analysis of a collaboration network in the field of computational geometry. ER - TY - CONF AU - Giatsidis, Christos AU - Thilikos, Dimitrios M. AU - Vazirgiannis, Michalis A2 - T1 - Evaluating Cooperation in Communities with the k-Core Structure T2 - Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on PB - CY - PY - 2011/ M2 - VL - IS - SP - 87 EP - 93 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5992567&tag=1 M3 - 10.1109/ASONAM.2011.65 KW - community KW - core KW - k-core L1 - SN - N1 - IEEE Xplore - Evaluating Cooperation in Communities with the k-Core Structure N1 - AB - Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs - such as DBLP and report interesting results. ER - TY - JOUR AU - Jäschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd T1 - Tag Recommendations in Social Bookmarking Systems JO - AI Communications PY - 2008/ VL - 21 IS - 4 SP - 231 EP - 247 UR - http://dx.doi.org/10.3233/AIC-2008-0438 M3 - 10.3233/AIC-2008-0438 KW - bookmark KW - core KW - folkrank KW - folksonomy KW - recommendations KW - social KW - tag L1 - SN - N1 - N1 - AB - Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.

In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of

user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.

ER - TY - CONF AU - Ahmed, Adel AU - Batagelj, Vladimir AU - Fu, Xiaoyan AU - Hong, Seok-Hee AU - Merrick, Damian AU - Mrvar, Andrej A2 - T1 - Visualisation and analysis of the internet movie database T2 - Visualization, 2007. APVIS '07. 2007 6th International Asia-Pacific Symposium on PB - CY - PY - 2007/feb. M2 - VL - IS - SP - 17 EP - 24 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4126213&tag=1 M3 - 10.1109/APVIS.2007.329304 KW - analysis KW - core KW - imdb L1 - SN - N1 - IEEE Xplore - Visualisation and analysis of the internet movie database N1 - AB - In this paper, we present a case study for the visualisation and analysis of large and complex temporal multivariate networks derived from the Internet movie database (IMDB). Our approach is to integrate network analysis methods with visualisation in order to address scalability and complexity issues. In particular, we defined new analysis methods such as (p,q)-core and 4-ring to identify important dense subgraphs and short cycles from the huge bipartite graphs. We applied island analysis for a specific time slice in order to identify important and meaningful subgraphs. Further, a temporal Kevin Bacon graph and a temporal two mode network are extracted in order to provide insight and knowledge on the evolution. ER - TY - CONF AU - Baur, Michael AU - Gaertler, Marco AU - Görke, Robert AU - Krug, Marcus AU - Wagner, Dorothea A2 - T1 - Generating Graphs with Predefined k-Core Structure T2 - Proceedings of the European Conference of Complex Systems PB - CY - PY - 2007/october M2 - VL - IS - SP - EP - UR - http://i11www.ira.uka.de/extra/publications/bggkw-ggpcs-07.pdf M3 - KW - analysis KW - core KW - generator KW - graph KW - structure L1 - SN - N1 - N1 - AB - The modeling of realistic networks is of great importance for modern complex systems research. Previous procedures typically model the natural growth of networks by means of iteratively adding nodes, geometric positioning information, a definition of link connectivity based on the preference for nearest neighbors or already highly connected nodes, or combine several of these approaches. Our novel model is based on the well-know concept of k-cores, originally introduced in social network analysis. Recent studies exposed the significant k-core structure of several real world systems, e.g. the AS network of the Internet. We present a simple and efficient method for generating networks which strictly adhere to the characteristics of a given k-core structure, called core fingerprint. We show-case our algorithm in a comparative evaluation with two well-known AS network generators. ER - TY - RPRT AU - Behrisch, Michael AU - Coja-Oghlan, Amin AU - Kang, Mihyun A2 - T1 - The order of the giant component of random hypergraphs PB - AD - PY - 2007/01 VL - IS - SP - EP - UR - http://www.informatik.hu-berlin.de/~coja/jlimit7.pdf M3 - KW - core KW - giant KW - hypergraph KW - kcore KW - order KW - random L1 - N1 - Aktuelle Veröffentlichungen - Algorithmen und Komplexität N1 - N1 - AB - ER - TY - GEN AU - Alvarez-Hamelin, Jose Ignacio AU - Dall'Asta, Luca AU - Barrat, Alain AU - Vespignani, Alessandro A2 - T1 - k-core decomposition: a tool for the analysis of large scale Internet graphs JO - PB - AD - PY - 2005/ VL - IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0511007 M3 - KW - analysis KW - core KW - decomposition KW - graph KW - kcore KW - network KW - toread L1 - N1 - [cs/0511007] k-core decomposition: a tool for the analysis of large scale Internet graphs N1 - AB - ER - TY - GEN AU - Batagelj, V. AU - Zaversnik, M. A2 - T1 - An O(m) Algorithm for Cores Decomposition of Networks JO - PB - AD - PY - 2003/ VL - IS - SP - EP - UR - http://arxiv.org/abs/cs/0310049 M3 - KW - core KW - graph KW - network L1 - N1 - [cs/0310049] An O(m) Algorithm for Cores Decomposition of Networks N1 - AB - The structure of large networks can be revealed by partitioning them to

smaller parts, which are easier to handle. One of such decompositions is based

on $k$--cores, proposed in 1983 by Seidman. In the paper an efficient, $O(m)$,

$m$ is the number of lines, algorithm for determining the cores decomposition

of a given network is presented. ER - TY - JOUR AU - Batagelj, V. AU - Zaversnik, M. T1 - Generalized Cores JO - CoRR PY - 2002/ VL - cs.DS/0202039 IS - SP - EP - UR - http://arxiv.org/abs/cs/0202039 M3 - KW - analysis KW - core KW - generalized KW - graph KW - kcore KW - network L1 - SN - N1 - N1 - AB - Cores are, besides connectivity components, one among few concepts that provides us with efficient decompositions of large graphs and networks. In the paper a generalization of the notion of core of a graph based on vertex property function is presented. It is shown that for the local monotone vertex property functions the corresponding cores can be determined in $O(m max ( log n))$ time. ER - TY - JOUR AU - Batagelj, Vladimir AU - Zaversnik, Matjaz T1 - Generalized Cores JO - CoRR PY - 2002/ VL - cs.DS/0202039 IS - SP - EP - UR - http://dblp.uni-trier.de/db/journals/corr/corr0202.html#cs-DS-0202039 M3 - KW - core KW - generalized KW - p-core L1 - SN - N1 - N1 - AB - ER - TY - GEN AU - Batagelj, Vladimir AU - Zaveršnik, Matjaž A2 - T1 - Generalized Cores JO - PB - AD - PY - 2002/ VL - IS - SP - EP - UR - http://arxiv.org/abs/cs/0202039 M3 - KW - core KW - generalized KW - p-core L1 - N1 - [cs/0202039] Generalized Cores N1 - AB - Cores are, besides connectivity components, one among few concepts that

provides us with efficient decompositions of large graphs and networks.

In the paper a generalization of the notion of core of a graph based on

vertex property function is presented. It is shown that for the local monotone

vertex property functions the corresponding cores can be determined in $O(m

max ( log n))$ time. ER - TY - CHAP AU - Batagelj, Vladimir AU - Mrvar, Andrej AU - Zaveršnik, Matjaž A2 - Kratochvíyl, Jan T1 - Partitioning Approach to Visualization of Large Graphs T2 - Graph Drawing PB - Springer CY - Berlin / Heidelberg PY - 1999/ VL - 1731 IS - SP - 90 EP - 97 UR - http://dx.doi.org/10.1007/3-540-46648-7_9 M3 - 10.1007/3-540-46648-7_9 KW - core KW - graph L1 - SN - 978-3-540-66904-3 N1 - Abstract - SpringerLink N1 - AB - The structure of large graphs can be revealed by partitioning graphs to smaller parts, which are easier to handle. In the paper we propose the use of core decomposition as an efficient approach for partitioning large graphs. On the selected subgraphs, computationally more intensive, clustering and blockmodeling can be used to analyze their internal structure. The approach is illustrated by an analysis of Snyder & Kick’s world trade graph. ER - TY - JOUR AU - Duquenne, Vincent T1 - The core of finite lattices JO - Discrete Math. PY - 1991/ VL - 87 IS - 2-3 SP - 133 EP - 147 UR - M3 - http://dx.doi.org/10.1016/0012-365X(91)90043-2 KW - cited KW - core KW - da KW - diplomarbeit KW - finite KW - lattice KW - scaffolding L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Duquenne, Vincent T1 - The core of finite lattices JO - Discrete Mathematics PY - 1991/ VL - 88 IS - 2-3 SP - 133 EP - 147 UR - http://www.sciencedirect.com/science/article/B6V00-45GMF6D-5/2/1120caa94c245d57b16992536b46325d M3 - 10.1016/0012-365X(91)90005-M KW - core KW - fca KW - lattice KW - scaffolding L1 - SN - N1 - ScienceDirect - Discrete Mathematics : The core of finite lattices N1 - AB - The meet-core of a finite lattice L is its minimal -- in fact minimum -- partial meet- subsemilattice of which the filter lattice is isomorphic to L. This gives a representation theory for finite lattices, in particular which extends Birkhoff's correspondence between ordered sets and distributive lattices, and is linked with Wille's notion of scaffolding. The meet-cores (and dually the join-cores) of modular, geometric and join-meet-distributive lattices are characterized locally by some obligatory sublattices or by some construction procedures otherwise. ER - TY - JOUR AU - Seidman, Stephen B. T1 - Network structure and minimum degree JO - Social Networks PY - 1983/ VL - 5 IS - 3 SP - 269 EP - 287 UR - http://www.sciencedirect.com/science/article/pii/037887338390028X M3 - 10.1016/0378-8733(83)90028-X KW - core KW - graph KW - network L1 - SN - N1 - ScienceDirect.com - Social Networks - Network structure and minimum degree N1 - AB - Social network researchers have long sought measures of network cohesion, Density has often been used for this purpose, despite its generally admitted deficiencies. An approach to network cohesion is proposed that is based on minimum degree and which produces a sequence of subgraphs of gradually increasing cohesion. The approach also associates with any network measures of local density which promise to be useful both in characterizing network structures and in comparing networks. ER -