@inproceedings{krause2008logsonomy, title = {Logsonomy - Social Information Retrieval with Logdata}, address = {New York, NY, USA}, author = {Beate Krause and Robert Jäschke and Andreas Hotho and Gerd Stumme}, booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, pages = {157--166}, publisher = {ACM}, year = 2008, url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia}, location = {Pittsburgh, PA, USA}, isbn = {978-1-59593-985-2}, doi = {http://doi.acm.org/10.1145/1379092.1379123}, abstract = {Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.}, biburl = {http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschke}, keywords = {2008 information engine myown retrieval analysis l3s network search social logsonomy for:nepomuk wp5} } @inproceedings{ahn2007topological, title = {Analysis of topological characteristics of huge online social networking services}, address = {New York, NY, USA}, author = {Yong-Yeol Ahn and Seungyeop Han and Haewoon Kwak and Sue Moon and Hawoong Jeong}, booktitle = {Proceedings of the 16th international conference on World Wide Web}, pages = {835--844}, publisher = {ACM}, year = 2007, url = {http://portal.acm.org/citation.cfm?id=1242685}, location = {Banff, Alberta, Canada}, isbn = {978-1-59593-654-7}, doi = {http://doi.acm.org/10.1145/1242572.1242685}, description = {Analysis of topological characteristics of huge online social networking services}, biburl = {http://www.bibsonomy.org/bibtex/2441b644b330fec7951c274a502c26e58/jaeschke}, keywords = {social folksonomy analysis network online} } @article{cattuto2007network, title = {Network Properties of Folksonomies}, author = {Ciro Cattuto and Christoph Schmitz and Andrea Baldassarri and Vito D. P. Servedio and Vittorio Loreto and Andreas Hotho and Miranda Grahl and Gerd Stumme}, editor = {Susanne Hoche and Andreas Nürnberger and Jürgen Flach}, journal = {AI Communications Journal, Special Issue on "Network Analysis in Natural Sciences and Engineering"}, number = 4, pages = {245-262}, publisher = {IOS Press}, volume = 20, year = 2007, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf}, issn = {0921-7126}, vgwort = {67}, biburl = {http://www.bibsonomy.org/bibtex/2da6c676c5664017247c7564fc247b190/jaeschke}, keywords = {property folksonomy network} } @misc{Ramos06, title = {On Self-Regulated Swarms, Societal Memory, Speed and Dynamics}, author = {Vitorino Ramos and Carlos Fernandes and Agostinho C. Rosa}, note = {arXiv:cs/0512002v1}, year = 2006, url = {http://arxiv.org/abs/cs/0512002}, id = {407750}, priority = {4}, conference = {International Conference on the Simulation and Synthesis of Living Systems}, abstract = {Wasps, bees, ants and termites all make effective use of their environment and resources by displaying collective "swarm" intelligence. Termite colonies - for instance - build nests with a complexity far beyond the comprehension of the individual termite, while ant colonies dynamically allocate labor to various vital tasks such as foraging or defense without any central decision-making ability. Recent research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions, as found in bacteria. What strikes from these observations is that both ant colonies and bacteria have similar natural mechanisms based on Stigmergy and Self-Organization in order to emerge coherent and sophisticated patterns of global foraging behavior. Keeping in mind the above characteristics we propose a Self-Regulated Swarm (SRS) algorithm which hybridizes the advantageous characteristics of Swarm Intelligence as the emergence of a societal environmental memory or cognitive map via collective pheromone laying in the landscape (properly balancing the exploration/exploitation nature of our dynamic search strategy), with a simple Evolutionary mechanism that trough a direct reproduction procedure linked to local environmental features is able to self-regulate the above exploratory swarm population, speeding it up globally. In order to test his adaptive response and robustness, we have recurred to different dynamic multimodal complex functions as well as to Dynamic Optimization Control problems, measuring reaction speeds and performance. Final comparisons were made with standard Genetic Algorithms (GAs), Bacterial Foraging strategies (BFOA), as well as with recent Co-Evolutionary approaches. SRS's were able to demonstrate quick adaptive responses, while outperforming the results obtained by the other approaches. Additionally, some successful behaviors were found: SRS was able to maintain a number of different solutions, while adapting to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes; the possibility to spontaneously create and maintain different subpopulations on different peaks, emerging different exploratory corridors with intelligent path planning capabilities; the ability to request for new agents (division of labor) over dramatic changing periods, and economizing those foraging resources over periods of intermediate stabilization. Finally, results illustrate that the present SRS collective swarm of bio-inspired ant-like agents is able to track about 65% of moving peaks traveling up to ten times faster than the velocity of a single individual composing that precise swarm tracking system. This emerged behavior is probably one of the most interesting ones achieved by the present work.}, biburl = {http://www.bibsonomy.org/bibtex/2804ad41798bd794f4f85c96bd6217127/jaeschke}, keywords = {social swarm cognition iccs_example network trias_example} } @techreport{Ramos05, title = {Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes}, author = {Vitorino Ramos and Carlos Fernandes and Agostinho C. Rosa}, institution = {Insituto Superior Técnico, Universidade Técnica de Lisboa}, note = {arXiv:nlin/0502057v1}, number = {CVRM-IST 127E-2005}, year = 2005, url = {http://arxiv.org/abs/nlin/0502057}, id = {407689}, priority = {4}, abstract = {Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with wich it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to Self-Organization, Stigmergy and Social Foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will adress the collective adaptation of a social community to a cultural (environmenatl, contextual) or media informational dynamical landscape, represented here - for the purpose of different experiments - by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes.}, biburl = {http://www.bibsonomy.org/bibtex/27debdcf93027a77b3b928caae4121dff/jaeschke}, keywords = {social perception swarm cognition iccs_example network trias_example} } @misc{candia-2007, title = {Uncovering individual and collective human dynamics from mobile phone records}, author = {J. Candia and M. C. Gonzalez and P. Wang and T. Schoenharl and G. Madey and A. L. Barabasi}, year = 2007, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0710.2939}, abstract = { Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.}, biburl = {http://www.bibsonomy.org/bibtex/2ea8b6a4442ccc0cb7dd222f6bd1d992a/jaeschke}, keywords = {sna dynamic phone social analysis network mobile} } @inproceedings{Brandes07Role, title = {Role-equivalent Actors in Networks}, author = {Ulrik Brandes and Jürgen Lerner}, booktitle = {ICFCA 2007 Satellite Workshop on Social Network Analysis and Conceptual Structures: Exploring Opportunities}, editor = {Sergei Obiedkov and Camille Roth}, year = 2007, url = {http://www.inf.uni-konstanz.de/algo/publications/bl-rean-07.pdf}, abstract = {Abstract. Communities in social networks are often defined as groups of densely connected actors. However, members of the same dense group are not equal but may differ largely in their social position or in the role they play. Furthermore, the same positions can be found across the borders of dense communities so that networks contain a significant group structure which does not coincide with the structure of dense groups. This papers gives a survey over formalizations of network-positions with a special emphasis on the use of algebraic notions.}, biburl = {http://www.bibsonomy.org/bibtex/26ea541158f972b850e9ea330b473c7c4/jaeschke}, keywords = {sna structure social role actor analysis network} } @inproceedings{Jason_Euzenat_2007, title = {Towards Semantic Social Networks}, address = {Berlin Heidelberg, Germany}, author = {Jason Jung and Jérôme Euzenat}, booktitle = {Proceedings of the European Semantic Web Conference (ESWC2007)}, editor = {Enrico Franconi and Michael Kifer and Wolfgang May}, month = {July}, pages = {267-280}, publisher = {Springer-Verlag}, series = {LNCS}, volume = 4519, year = 2007, url = {http://www.eswc2007.org/pdf/eswc07-jung.pdf}, biburl = {http://www.bibsonomy.org/bibtex/2d628e5621e97187b71d54bf3c2700670/jaeschke}, keywords = {sna 2007 social eswc semantic network} } @inproceedings{1244292, title = {FCA-based approach for mining contextualized folksonomy}, address = {New York, NY, USA}, author = {Hak Lae Kim and Suk Hyung Hwang and Hong Gee Kim}, booktitle = {SAC '07: Proceedings of the 2007 ACM symposium on Applied computing}, pages = {1340--1345}, publisher = {ACM Press}, year = 2007, url = {http://portal.acm.org/citation.cfm?id=1244002.1244292&coll=GUIDE&dl=}, location = {Seoul, Korea}, isbn = {1-59593-480-4}, doi = {http://doi.acm.org/10.1145/1244002.1244292}, biburl = {http://www.bibsonomy.org/bibtex/24440c3ca148004f3759456eac34e84fa/jaeschke}, keywords = {mining formal concept tagging social folksonomy analysis network fca} } @article{duch-2005-72, title = {Community detection in complex networks using Extremal Optimization}, author = {J. Duch and A. Arenas}, journal = {Physical Review E}, pages = 027104, volume = 72, year = 2005, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0501368}, description = {Citebase - Community detection in complex networks using Extremal Optimization}, abstract = {We propose a novel method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature. We present the results of the algorithm for computer simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.}, biburl = {http://www.bibsonomy.org/bibtex/236d905c5223e5516db9d08eb3e0bc9fc/jaeschke}, keywords = {complex detection community network} } @misc{batagelj-2002, title = {Generalized Cores}, author = {V. Batagelj and M. Zaversnik}, note = {cs.DS/0202039}, year = 2002, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0202039}, description = {[cs/0202039] Generalized Cores}, abstract = {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 $O(m \max (\Delta, \log n))$ time.}, biburl = {http://www.bibsonomy.org/bibtex/204dd5c8a505463b1e196f842b91a8b07/jaeschke}, keywords = {graph kcore core analysis network generalized} } @article{dorogovtsev-2006-96, title = {k-core organization of complex networks}, author = {S. N. Dorogovtsev and A. V. Goltsev and J. F. F. Mendes}, journal = {Physical Review Letters}, pages = 040601, volume = 96, year = 2006, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0509102}, description = {[cond-mat/0509102] k-core organization of complex networks}, biburl = {http://www.bibsonomy.org/bibtex/2da319f745eb44dfd197ccddab3384024/jaeschke}, keywords = {graph decomposition kcore analysis network} } @misc{alvarezhamelin-2005, title = {k-core decomposition: a tool for the analysis of large scale Internet graphs}, author = {Jose Ignacio Alvarez-Hamelin and Luca Dall'Asta and Alain Barrat and Alessandro Vespignani}, year = 2005, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0511007}, description = {[cs/0511007] k-core decomposition: a tool for the analysis of large scale Internet graphs}, biburl = {http://www.bibsonomy.org/bibtex/2ea1566a1e88a30950615c7d660a9eb6f/jaeschke}, keywords = {graph decomposition kcore core toread analysis network} } @inproceedings{hoser2006semantic, title = {Semantic Network Analysis of Ontologies}, author = {Bettina Hoser and Andreas Hotho and Robert Jäschke and Christoph Schmitz and Gerd Stumme}, booktitle = {The Semantic Web: Research and Applications}, month = {June}, note = {Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, year = 2006, abstract = {A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.}, biburl = {http://www.bibsonomy.org/bibtex/29a2c77c7c7a1b19cd16df08cca65f706/jaeschke}, keywords = {2006 semantic myown ontology l3s analysis iccs_example network trias_example} } @inproceedings{1145629, title = {Divide and conquer approach for efficient pagerank computation}, address = {New York, NY, USA}, author = {Prasanna Kumar Desikan and Nishith Pathak and Jaideep Srivastava and Vipin Kumar}, booktitle = {ICWE '06: Proceedings of the 6th international conference on Web engineering}, month = {July}, pages = {233--240}, publisher = {ACM Press}, year = 2006, url = {http://portal.acm.org/citation.cfm?doid=1145581.1145629}, location = {Palo Alto, California, USA}, isbn = {1-59593-352-2}, doi = {http://doi.acm.org/10.1145/1145581.1145629}, description = {Divide and conquer approach for efficient pagerank computation}, biburl = {http://www.bibsonomy.org/bibtex/232b98aca2e38ee638d3aea77dddea2a2/jaeschke}, keywords = {pagerank analysis toread ranking network} } @book{wasserman_faust_94, title = {Social Network Analysis: Methods and Applications}, author = {Stanley Wasserman and Katherine Faust and Dawn Iacobucci and Mark Granovetter}, publisher = {Cambridge University Press}, year = 1994, biburl = {http://www.bibsonomy.org/bibtex/276dc2c519de8e00a07fcb27aeda6e472/jaeschke}, keywords = {sna social analysis network} } @phdthesis{trier05visualization, title = {IT-supported Visualization and Evaluation of Virtual Knowledge Communities. Applying Social Network Intelligence Software in Knowledge Management to enable knowledge oriented People Network Management}, author = {Matthias Trier}, year = 2005, url = {http://nbn-resolving.de/urn/resolver.pl?urn=urn:nbn:de:kobv:83-opus-10720}, biburl = {http://www.bibsonomy.org/bibtex/266eb70a04e6946077182446170dd6dcf/jaeschke}, keywords = {knowledge detection social community management network} } @inproceedings{citeulike:391307, title = {The author-topic model for authors and documents}, address = {Arlington, VA, USA}, author = {Michal Rosen-Zvi and Thomas Griffiths and Mark Steyvers and Padhraic Smyth}, booktitle = {Proceedings of the 20th conference on Uncertainty in artificial intelligence}, pages = {487--494}, publisher = {AUAI Press}, year = 2004, url = {http://portal.acm.org/citation.cfm?id=1036843.1036902}, id = {391307}, priority = {0}, isbn = {0974903906}, biburl = {http://www.bibsonomy.org/bibtex/2a4dd688efe5778fb99ff94de104211aa/jaeschke}, keywords = {topicinference social community socialnets network} } @inproceedings{popescul01probabilistic, title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments}, address = {Seattle, Washington}, author = {Alexandrin Popescul and Lyle Ungar and David Pennock and Steve Lawrence}, booktitle = {17th Conference on Uncertainty in Artificial Intelligence}, month = {August 2--5}, pages = {437--444}, year = 2001, url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html}, description = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments - Popescul, Ungar, Pennock, Lawrence (ResearchIndex)}, abstract = {Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...}, biburl = {http://www.bibsonomy.org/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke}, keywords = {todo three network 3mode mode clustering} } @article{dh72algorithms, title = {Algorithms for partitioning graphs and computer logic based on eigenvectors of connection matrices}, author = {W.E. Donath and A.J. Hoffman}, journal = {IBM Technical Disclosure Bulletin}, number = 3, pages = {938-944}, volume = 15, year = 1972, biburl = {http://www.bibsonomy.org/bibtex/20302cd05477d3be28a12fa1b2c1d1151/jaeschke}, keywords = {graph matrix network clustering} }