@article{dorogovtsev01, title = {Size-dependent degree distribution of a scale-free growing network}, author = {S. N. Dorogovtsev and J. F. F. Mendes and A. N. Samukhin}, journal = {Phys. Rev. E}, month = {May}, number = 6, pages = 062101, publisher = {American Physical Society}, volume = 63, year = 2001, numpages = {4}, doi = {10.1103/PhysRevE.63.062101}, biburl = {http://www.bibsonomy.org/bibtex/2e5427464b2bd3f54c97165b9110863eb/schmitz}, keywords = {socialnetwork smallworld scalefree graphtheory network} } @article{cattuto2007network, title = {Network Properties of Folksonomies}, author = {Ciro Cattuto and Christoph Schmitz and Andre Baldassarri and Vito D. P. Servedio and Vittorio Loreto and Andreas Hotho and Miranda Grahl and Gerd Stumme}, journal = {AI Communications Special Issue on "Network Analysis in Natural Sciences and Engineering" (to appear)}, year = 2007, biburl = {http://www.bibsonomy.org/bibtex/2d7a5f75c14ced45ca76bad1e9ef162eb/schmitz}, keywords = {sna socialnetwork tagging} } @inproceedings{wang2006improving, title = {Improving cooperation in peer-to-peer systems using social networks}, author = {Wenyu Wang and Li Zhao and Ruixi Yuan}, booktitle = {Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International}, year = 2006, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=34366&arnumber=1639703&count=468&index=445}, isbn = {1-4244-0054-6}, doi = {10.1109/IPDPS.2006.1639703}, description = {Improving cooperation in peer-to-peer systems using social networks}, abstract = {Rational and selfish nodes in P2P systems usually lack effective incentives to cooperate, contributing to the increase of free-riders, and degrading the system performance. Various attacks such as whitewashing, collusion, and software cracking pose great challenges on distributed reputation management. To tackle these problems, we propose to build a social network on P2P system, and use the strength of social connections to facilitate transactions in P2P system. The' small world' character of social networks makes it feasible for nodes to locate resources and conduct transactions while maintain limited local memory history. Such distributed memory combined by relationship between peers constructs a powerful reputation management network, which could have better performance than shared history system and is more robust under various attacks. Our simulation and analysis show that the social network model can greatly incent cooperation in P2P networks and enormously reduce the memory cost.}, biburl = {http://www.bibsonomy.org/bibtex/28a5704ad4f7a7b741cf953fc65422264/schmitz}, keywords = {socialnetwork p2p} } @inproceedings{DBLP:conf/kdd/FastJL05, title = {Creating social networks to improve peer-to-peer networking.}, author = {Andrew Fast and David Jensen and Brian Neil Levine}, booktitle = {KDD}, crossref = {DBLP:conf/kdd/2005}, editor = {Robert Grossman and Roberto Bayardo and Kristin P. Bennett}, pages = {568-573}, publisher = {ACM}, year = 2005, ee = {http://doi.acm.org/10.1145/1081870.1081938}, bibsource = {DBLP, http://dblp.uni-trier.de}, isbn = {1-59593-135-X}, description = {DBLP Record 'conf/kdd/FastJL05'}, biburl = {http://www.bibsonomy.org/bibtex/2f195f8f225417041257c263d824894a6/schmitz}, keywords = {socialnetwork p2p} } @article{mika2005a, title = {{Social Networks and the Semantic Web: The Next Challenge}}, author = {P. Mika}, journal = {IEEE Intelligent Systems}, month = {January/February}, number = 1, volume = 20, year = 2005, url = {http://www.cs.vu.nl/~pmika/research/papers/IEEE-TrendsAndControversies.pdf}, biburl = {http://www.bibsonomy.org/bibtex/20cc701059bf83b7b88f19f0fbbc758df/schmitz}, keywords = {socialnetwork semanticweb mika} } @article{gn02community, title = {Community structure in social and biological networks}, author = {Michelle Girvan and M.E.J. Newman}, journal = {Proceedings of the National Academy of Science}, number = 12, pages = {7821-7826}, volume = 99, year = 2002, abstract = {A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known---a collaboration network and a food web---and find that it detects significant and informative community divisions in both cases.}, biburl = {http://www.bibsonomy.org/bibtex/28f80a8586927ea69ea915b6c32e87629/schmitz}, keywords = {newman socialnetwork smallworld graphtheory community girvan clustco} } @inproceedings{newman-random, title = {Random graph models of social networks}, author = {M. Newman and D. Watts and S. Strogatz}, booktitle = {Proc. Natl. Acad. Sci., to appear.}, year = 2001, url = {citeseer.ist.psu.edu/445095.html}, biburl = {http://www.bibsonomy.org/bibtex/25f2a82f1c7a71c35f51e7ca51ad15226/schmitz}, keywords = {newman model socialnetwork smallworld watts graphtheory strogatz} } @article{newman03fast, title = {Fast algorithm for detecting community structure in networks}, author = {M.E.J. Newman}, journal = {Physical Review E}, month = {September}, volume = 69, year = 2003, url = {http://arxiv.org/abs/cond-mat/0309508}, biburl = {http://www.bibsonomy.org/bibtex/256de7e6d214faebdbf2f2ef0fce09d7d/schmitz}, keywords = {socialnetwork graphtheory community network clustering algorithm} } @misc{citeulike:95936, title = {Finding community structure in very large networks}, author = {Aaron Clauset and M. E. J. Newman and Cristopher Moore}, month = {August}, year = 2004, url = {http://arxiv.org/abs/cond-mat/0408187}, id = {95936}, priority = {3}, comment = {c++ source code here: http://www.cs.unm.edu/~aaron/research/fastmodularity.htm}, eprint = {cond-mat/0408187}, abstract = {The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in which case our algorithm runs in essentially linear time, O(n log^2 n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web-site of a large online retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400,000 vertices and 2 million edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.}, biburl = {http://www.bibsonomy.org/bibtex/2f9a12630a6d31d576ea5222219a4cf0b/schmitz}, keywords = {sna newman socialnetwork community clauset girvan} } @inproceedings{conf/focs/KempeK02, title = {Protocols and Impossibility Results for Gossip-Based Communication Mechanisms.}, author = {David Kempe and Jon M. Kleinberg}, booktitle = {FOCS}, crossref = {conf/focs/2002}, pages = {471-480}, publisher = {IEEE Computer Society}, year = 2002, url = {http://dblp.uni-trier.de/db/conf/focs/focs2002.html#KempeK02}, ee = {http://www.computer.org/proceedings/focs/1822/18220471abs.htm}, isbn = {0-7695-1822-2}, date = {2003-01-24}, description = {dblp}, biburl = {http://www.bibsonomy.org/bibtex/2b8d36793a5b0e675b8a06ad3bd761593/schmitz}, keywords = {sna socialnetwork kleinberg graphtheory routing} } @inproceedings{citeulike:115243, title = {Maximizing the spread of influence through a social network}, author = {David Kempe and Jon Kleinberg and \&\#201;va Tardos}, booktitle = {KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining}, pages = {137--146}, publisher = {ACM Press}, year = 2003, url = {http://dx.doi.org/10.1145/956750.956769}, id = {115243}, priority = {2}, isbn = {1581137370}, doi = {10.1145/956750.956769}, biburl = {http://www.bibsonomy.org/bibtex/206dc73dd414e3a64183a6ecfeb05a7a6/schmitz}, keywords = {sna socialnetwork kleinberg graphtheory} } @article{krackhardt1993informal, title = {Informal Networks: The Company Behind the Chart}, author = {David Krackhardt and Jeffrey Hanson}, journal = {Harvard Business Review}, month = {jul/aug}, number = 4, pages = {104-111}, volume = 71, year = 1993, date = {(July / August(4)):}, biburl = {http://www.bibsonomy.org/bibtex/24a6df9c5562f6fddb58686e51bc8a215/schmitz}, keywords = {socialnetwork knowledgemanagement} } @article{krackhardt, title = {Informelle Netze: Die heimlichen Kraftquellen}, author = {David Krackhardt and Jeffrey Hanson}, journal = {Harvard Business Manager}, pages = {16--24}, volume = 1, year = 1994, description = {Literatur soziale Netzwerke}, biburl = {http://www.bibsonomy.org/bibtex/2b202157b0852219765cb2e1e7bf3e6ac/schmitz}, keywords = {socialnetwork knowledgemanagement} } @book{surowiecki2004, title = {The wisdom of crowds}, author = {J. Surowiecki}, publisher = {Doubleday}, year = 2004, biburl = {http://www.bibsonomy.org/bibtex/2b78f7a737aa99ffad5d1862f8ef478ae/schmitz}, keywords = {sna socialnetwork} }