@article{atzmueller2015descriptionoriented, abstract = {Abstract Communities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided. For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph’s nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the “description space” characterizing interesting sets of nodes (i.e., subgroups) in the “graph space”; the interestingness of a community is evaluated by a selectable quality measure. We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications. }, author = {Atzmueller, Martin and Doerfel, Stephan and Mitzlaff, Folke}, doi = {http://dx.doi.org/10.1016/j.ins.2015.05.008}, interhash = {d87cc381289cd86387b81ff5b8646cb5}, intrahash = {fb7a824e273ab34db22f49d54b5d1e12}, issn = {0020-0255}, journal = {Information Sciences }, number = 0, pages = { - }, title = {Description-oriented community detection using exhaustive subgroup discovery }, url = {http://www.sciencedirect.com/science/article/pii/S0020025515003667}, year = 2015 } @incollection{mitzlaff2011community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups.}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, doi = {10.1007/978-3-642-23599-3_5}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {a1c0fd5a9f8c5ddb33b3196927409f36}, isbn = {978-3-642-23598-6}, language = {English}, pages = {79-98}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Community Assessment Using Evidence Networks}, url = {http://dx.doi.org/10.1007/978-3-642-23599-3_5}, volume = 6904, year = 2011 } @misc{mitzlaff2013userrelatedness, abstract = {With social media and the according social and ubiquitous applications finding their way into everyday life, there is a rapidly growing amount of user generated content yielding explicit and implicit network structures. We consider social activities and phenomena as proxies for user relatedness. Such activities are represented in so-called social interaction networks or evidence networks, with different degrees of explicitness. We focus on evidence networks containing relations on users, which are represented by connections between individual nodes. Explicit interaction networks are then created by specific user actions, for example, when building a friend network. On the other hand, more implicit networks capture user traces or evidences of user actions as observed in Web portals, blogs, resource sharing systems, and many other social services. These implicit networks can be applied for a broad range of analysis methods instead of using expensive gold-standard information. In this paper, we analyze different properties of a set of networks in social media. We show that there are dependencies and correlations between the networks. These allow for drawing reciprocal conclusions concerning pairs of networks, based on the assessment of structural correlations and ranking interchangeability. Additionally, we show how these inter-network correlations can be used for assessing the results of structural analysis techniques, e.g., community mining methods.}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, interhash = {40aa075d925f2e6e009986fd9e60b11b}, intrahash = {cbed5fadde51ddb20c6a470ced93556a}, note = {cite arxiv:1309.3888}, title = {User-Relatedness and Community Structure in Social Interaction Networks}, url = {http://arxiv.org/abs/1309.3888}, year = 2013 } @incollection{ADHMS:12, address = {Heidelberg, Germany}, alteditor = {Editor}, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {{Modeling and Mining Ubiquitous Social Media}}, interhash = {4f1f4b515b01cc448a91b3e368deabad}, intrahash = {d81d6f6ccdf3ff6572898d39c90e6354}, publisher = {Springer Verlag}, series = {LNAI}, title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles}, url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-face-to-face-contacts-dynamics-lnai-2012.pdf}, volume = 7472, year = 2012 } @incollection{ADHMS:12, address = {Heidelberg, Germany}, alteditor = {Editor}, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {{Modeling and Mining Ubiquitous Social Media}}, interhash = {4f1f4b515b01cc448a91b3e368deabad}, intrahash = {d81d6f6ccdf3ff6572898d39c90e6354}, publisher = {Springer Verlag}, series = {LNAI}, title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles}, url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-face-to-face-contacts-dynamics-lnai-2012.pdf}, volume = 7472, year = 2012 } @inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {0f45e870093c053e6f41f54c14bda46b}, series = {LNAI}, title = {{Community Assessment using Evidence Networks}}, volume = 6904, year = 2011 } @inproceedings{ADHMS:11, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011}, interhash = {49e97def917e352ca21ab2e3eb7bd88a}, intrahash = {1fe037ea2712b205c564243d67840059}, title = {Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players}, year = 2011 } @inproceedings{ADHMS:11, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011}, interhash = {49e97def917e352ca21ab2e3eb7bd88a}, intrahash = {1fe037ea2712b205c564243d67840059}, title = {Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players}, year = 2011 } @inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {0f45e870093c053e6f41f54c14bda46b}, series = {LNAI}, title = {{Community Assessment using Evidence Networks}}, volume = 6904, year = 2011 } @inproceedings{ADHMS:11, address = {to appear}, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011}, interhash = {49e97def917e352ca21ab2e3eb7bd88a}, intrahash = {1fe037ea2712b205c564243d67840059}, title = {{Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players}}, year = 2011 } @inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {0f45e870093c053e6f41f54c14bda46b}, series = {LNAI}, title = {{Community Assessment using Evidence Networks}}, volume = 6904, year = 2011 } @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, interhash = {75fbc00000a1bd7ca5f93ca1d24d62c5}, intrahash = {34d79867b23f41ca2e9f481ee894630f}, title = {Community Assessment using Evidence Networks}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, year = 2010 } @inproceedings{atzmueller2010towards, author = {Atzmueller, Martin and Mitzlaff, Folke}, booktitle = {Workshop on Mining Patterns and Subgroups}, interhash = {3669737df3d351bf27636b9dc07c13ee}, intrahash = {2f61938116e8b239fbff58520ecc284c}, publisher = {Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award}, title = {{Towards Mining Descriptive Community Patterns}}, year = 2010 } @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, interhash = {6d5b307fdf8ceb7dc191788fc4b61299}, intrahash = {d93c7cc72cd40e027d5421fdafe90a9a}, title = {Community Assessment using Evidence Networks}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, year = 2010 } @inproceedings{atzmueller2011efficient, author = {Atzmueller, Martin and Mitzlaff, Folke}, booktitle = {Proc. 24th Intl. FLAIRS Conference}, interhash = {d70fe826823693b5fd86be48501f2d23}, intrahash = {be5788e3988f2edeb022f5fbd12097a5}, publisher = {AAAI Press}, title = {Efficient Descriptive Community Mining}, year = 2011 } @inproceedings{atzmueller2011facetoface, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011}, interhash = {49e97def917e352ca21ab2e3eb7bd88a}, intrahash = {1fe037ea2712b205c564243d67840059}, title = {Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players}, year = 2011 } @inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, interhash = {1ef065a81ed836dfd31fcc4cd4da133b}, intrahash = {0f45e870093c053e6f41f54c14bda46b}, series = {LNAI}, title = {{Community Assessment using Evidence Networks}}, volume = 6904, year = 2011 } @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world socialbookmarking application BibSonomy. The results indicate that the evidencenetworks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, file = {:mitzlaff2010community.pdf:PDF}, interhash = {75fbc00000a1bd7ca5f93ca1d24d62c5}, intrahash = {34d79867b23f41ca2e9f481ee894630f}, title = {Community Assessment using Evidence Networks}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2010community.pdf}, year = 2010 } @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, interhash = {75fbc00000a1bd7ca5f93ca1d24d62c5}, intrahash = {34d79867b23f41ca2e9f481ee894630f}, title = {Community Assessment using Evidence Networks}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, year = 2010 } @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, interhash = {75fbc00000a1bd7ca5f93ca1d24d62c5}, intrahash = {34d79867b23f41ca2e9f481ee894630f}, title = {Community Assessment using Evidence Networks}, url = {http://www.kde.cs.uni-kassel.de/ws/muse2010}, year = 2010 } @inproceedings{mitzlaff2010visit, abstract = {The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end. Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.}, address = {New York, NY, USA}, author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas}, booktitle = {HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810664}, interhash = {5584c4c57fcd8eb4663df8b114bcf09c}, intrahash = {6628bf43e3834ba147a22992f2f534e9}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, pages = {265--270}, publisher = {ACM}, title = {Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy}, url = {http://portal.acm.org/citation.cfm?id=1810617.1810664}, year = 2010 }