BibSonomyburst/user/hotho/networkBibSonomy RSS feed for /user/hotho/network2012-02-16T03:18:24+01:00http://www.bibsonomy.org/bibtex/2ed618f45800255b5a5179d36849cd0b4/hothohotho2011-11-04T17:35:31+01:00network paper publication ranking scientific toread <span class="authorEditorList"><a href="/author/Walker">Dylan Walker</a>, <a href="/author/Xie">Huafeng Xie</a>, <a href="/author/Yan">Koon-Kiu Yan</a>, and <a href="/author/Maslov">Sergei Maslov</a> </span><em>Journal of Statistical Mechanics: Theory and Experiment</em> <em>2007(06):P06010</em> (<em>2007</em>)Fri Nov 04 17:35:31 CET 2011Journal of Statistical Mechanics: Theory and Experiment06P06010Ranking scientific publications using a model of network traffic20072007network paper publication ranking scientific toread To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.http://www.bibsonomy.org/bibtex/2a6ef16ba759ee4c56ccd4d017560344e/hothohotho2011-11-02T15:16:51+01:00anaylsis bots facebook network socialbot socialize toread <span class="authorEditorList"><a href="/author/Boshmaf">Yazan Boshmaf</a>, <a href="/author/Muslukhov">Ildar Muslukhov</a>, <a href="/author/Beznosov">Konstantin Beznosov</a>, and <a href="/author/Ripeanu">Matei Ripeanu</a> </span><em>Proc. of the Annual Computer Security Applications Conference 2011, </em><em>ACM, </em>(<em>2011</em>)Wed Nov 02 15:16:51 CET 2011Proc. of the Annual Computer Security Applications Conference 2011The Socialbot Network: When Bots Socialize for Fame and Money2011anaylsis bots facebook network socialbot socialize toread Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start o by inltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale inltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are or- chestrated in a command-and-control fashion. We operated such an SbN on Facebook|a 750 million user OSN|for about 8 weeks. We collected data related to users' behav- ior in response to a large-scale inltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be inltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful inltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in prac- tice, OSN security defenses, such as the Facebook Immune System, are not eective enough in detecting or stopping a large-scale inltration as it occurs.http://www.bibsonomy.org/bibtex/240c3dea03e3e4c561db6bc4b34c6f3da/hothohotho2011-10-05T12:00:13+02:00factors network social toread <span class="authorEditorList"><a href="/author/Tran">D.N. Tran</a>, <a href="/author/Min">B. Min</a>, <a href="/author/Li">J. Li</a>, and <a href="/author/Subramanian">L. Subramanian</a> </span><em>Citeseer, </em>(<em>2009</em>)Wed Oct 05 12:00:13 CEST 2011Sybil-resilient online content rating2009factors network social toread http://www.bibsonomy.org/bibtex/2eb4553d07c2975a62fff33e92646a7df/hothohotho2011-09-07T10:03:04+02:00analysis authority bibsonomy network peer toread <span class="authorEditorList"><a href="/author/Anagnostopoulos">Aris Anagnostopoulos</a>, <a href="/author/Brova">George Brova</a>, and <a href="/author/Terzi">Evimaria Terzi</a> </span><em>Proceedings of the ECML/PKDD 2011, </em>(<em>2011</em>)Wed Sep 07 10:03:04 CEST 2011Proceedings of the ECML/PKDD 2011Peer and Authority Pressure in Information-Propagation Models2011analysis authority bibsonomy network peer toread http://www.bibsonomy.org/bibtex/28afd9e99551c5fc1343fcc47542dbef6/hothohotho2011-02-18T13:52:47+01:00facebook network structure toread <span class="authorEditorList"><a href="/author/Traud">Amanda L. Traud</a>, <a href="/author/Mucha">Peter J. Mucha</a>, and <a href="/author/Porter">Mason A. Porter</a> </span>(<em>2011</em>)<em>cite arxiv:1102.2166 Comment: 82 pages including many pages of tables, 8 multi-part figures, &amp;quot;Facebook100&amp;quot; data used in this paper is publicl<span class="info">...<div>cite arxiv:1102.2166 Comment: 82 pages including many pages of tables, 8 multi-part figures, &amp;quot;Facebook100&amp;quot; data used in this paper is publicly available at http://people.maths.ox.ac.uk/~porterm/data/facebook100.zip</div></span> . </em>Fri Feb 18 13:52:47 CET 2011cite arxiv:1102.2166 Comment: 82 pages (including many pages of tables), 8 multi-part figures, &quot;Facebook100&quot; data used in this paper is publicly available at http://people.maths.ox.ac.uk/~porterm/data/facebook100.zipSocial Structure of Facebook Networks2011facebook network structure toread We study the social structure of Facebook &quot;friendship&quot; networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes - gender, class year, major, high school, and residence - at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further. http://www.bibsonomy.org/bibtex/2c4c90214919c4edb8da5d69b78e5180b/hothohotho2011-02-09T15:33:32+01:00data network representation toread <span class="authorEditorList"><a href="/author/Hill">Shawndra Hill</a>, <a href="/author/Agarwal">Deepak K. Agarwal</a>, <a href="/author/Bell">Robert Bell</a>, and <a href="/author/Volinsky">Chris Volinsky</a> </span><em>Journal of Computational &amp;#38; Graphical Statistics</em> (<em>September 2006</em>)Wed Feb 09 15:33:32 CET 2011Journal of Computational &#38; Graphical StatisticsSeptember 584-608(25)Building an Effective Representation for Dynamic Networks152006data network representation toread A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks. http://www.bibsonomy.org/bibtex/2f8d7bc2af5753906dc3897196daac18c/hothohotho2010-07-08T12:57:16+02:002010 data introduction mining myown network semantic social web <span class="authorEditorList"><a href="/author/Berendt">Bettina Berendt</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>8(2-3):95 - 96</em> (<em>2010</em>)<em>Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Gra<span class="info">...<div>Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences</div></span> . </em>Thu Jul 08 12:57:16 CEST 2010Web Semantics: Science, Services and Agents on the World Wide WebBridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences2-395 - 96Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0820102010 data introduction mining myown network semantic social web http://www.bibsonomy.org/bibtex/237b13a9085306104ac242a9595cb76bd/hothohotho2010-02-19T13:49:58+01:00network rfid toread <span class="authorEditorList"><a href="/author/Paradiso">Joseph Paradiso</a>, <a href="/author/Gips">Jonathan Gips</a>, <a href="/author/Laibowitz">Mathew Laibowitz</a>, <a href="/author/Sadi">Sajid Sadi</a>, <a href="/author/Merrill">David Merrill</a>, <a href="/author/Aylward">Ryan Aylward</a>, <a href="/author/Maes">Pattie Maes</a>, and <a href="/author/Pentland">Alex Pentland</a> </span><em>Personal and Ubiquitous Computing</em> <em>14(2):137--152</em> (<em>February 2010</em>)Fri Feb 19 13:49:58 CET 2010Personal and Ubiquitous Computing#feb#2137--152Identifying and facilitating social interaction with a wearable wireless sensor network142010network rfid toread Abstract&nbsp;&nbsp;We have designed a highly versatile badge system to facilitate a variety of interaction at large professional or social events and serve as a platform for conducting research into human dynamics. The badges are equipped with a large LED display, wirelessinfrared and radio frequency networking, and a host of sensors to collect data that we have used to develop features and algorithmsaimed at classifying and predicting individual and group behavior. This paper overviews our badge system, describes the interactionsand capabilities that it enabled for the wearers, and presents data collected over several large deployments. This data isanalyzed to track and socially classify the attendees, predict their interest in other people and demonstration installations,profile the restlessness of a crowd in an auditorium, and otherwise track the evolution and dynamics of the events at whichthe badges were run.SpringerLink - Zeitschriftenbeitraghttp://www.bibsonomy.org/bibtex/218a1220e45e38620051a0c9b854d1a28/hothohotho2010-02-07T19:54:49+01:00analysis centrality network social toread <span class="authorEditorList"><a href="/author/Kitsak">Maksim Kitsak</a>, <a href="/author/Gallos">Lazaros K. Gallos</a>, <a href="/author/Havlin">Shlomo Havlin</a>, <a href="/author/Liljeros">Fredrik Liljeros</a>, <a href="/author/Muchnik">Lev Muchnik</a>, <a href="/author/Stanley">H. Eugene Stanley</a>, and <a href="/author/Makse">Hernan A. Makse</a> </span>(<em>2010</em>)<em>cite arxiv:1001.5285 Comment: 31 pages, 12 figures . </em>Sun Feb 07 19:54:49 CET 2010cite arxiv:1001.5285 Comment: 31 pages, 12 figuresIdentifying influential spreaders in complex networks2010analysis centrality network social toread Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient &quot;spreaders&quot; in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people (high betweenness centrality). Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that-- in the case of infections that do not confer immunity on recovered individuals-- the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies. Identifying influential spreaders in complex networkshttp://www.bibsonomy.org/bibtex/265c6f348a54f872fb3e60b4bd64b485b/hothohotho2010-01-31T12:16:26+01:00collaborative hypergraph network tagging taggingsurvey <span class="authorEditorList"><a href="/author/Lambiotte">R. Lambiotte</a>, and <a href="/author/Ausloos">M. Ausloos</a> </span>(<em>2005</em>)<em>cite arxiv:cs.DS/0512090 . </em>Sun Jan 31 12:16:26 CET 2010cite arxiv:cs.DS/0512090 Collaborative tagging as a tripartite network2005collaborative hypergraph network tagging taggingsurvey We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed. [cs/0512090] Collaborative tagging as a tripartite networkhttp://www.bibsonomy.org/bibtex/2a446dfd22b95fd3e108fb11caf1669ae/hothohotho2009-07-24T21:16:35+02:00architectures network search searching social <span class="authorEditorList"><a href="/author/Trier">M. Trier</a>, and <a href="/author/Bobrik">A. Bobrik</a> </span><em>IEEE Internet Computing</em> <em>13(2):51--59</em> (<em>2009</em>)Fri Jul 24 21:16:35 CEST 2009IEEE Internet Computing251--59{Social Search: Exploring and Searching Social Architectures in Digital Networks}132009architectures network search searching social http://www.bibsonomy.org/bibtex/2602e2e19ec9de91f4f992cd1486bc0df/hothohotho2009-07-17T11:54:31+02:00info2.0 network social systems toread <span class="authorEditorList"><a href="/author/Heidemann">Julia Heidemann</a> </span><em>Informatik-Spektrum</em> (<em>2009</em>)Fri Jul 17 11:54:31 CEST 2009Informatik-Spektrum--Online Social Networks – Ein sozialer und technischer Überblick2009info2.0 network social systems toread Zusammenfassung&nbsp;&nbsp;Online Social Networks wie Xing.com oder Facebook.com gehören zu den am stärksten wachsenden Diensten im Internet. Im Jahr 2008 nutzten geschätzte 580 Mio. Menschen weltweit diese Angebote. Entsprechend schnell haben sich Online Social Networksinnerhalb weniger Jahre von einem Nischenphänomen zu einem weltweiten Medium der IT-gestützten Kommunikation entwickelt. Insbesondereaufgrund stark wachsender Mitgliederzahlen entfalten Online Social Networks eine erhebliche gesellschaftliche und wirtschaftlicheBedeutung. Vor diesem Hintergrund ist es Ziel dieses Beitrags, Begriff und Eigenschaften, Entstehung und Entwicklung sowieNutzenpotenziale und Herausforderungen von Online Social Networks näher zu untersuchen.SpringerLink - Zeitschriftenbeitraghttp://www.bibsonomy.org/bibtex/2c8dbb6371be8d67e3aa1928bd3dd0fed/hothohotho2009-05-15T17:22:38+02:002006 analysis association folksonomy kdubiq myown network rules semantic seminar2006 sosbuch summerschool <span class="authorEditorList"><a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Data Science and Classification Proc. IFCS 2006 Conference, </em><em>page 261-270. </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>July 2006</em>)<em>Ljubljana . </em>Fri May 15 17:22:38 CEST 2009Berlin/HeidelbergData Science and Classification (Proc. IFCS 2006 Conference)JulyLjubljana261-270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 analysis association folksonomy kdubiq myown network rules semantic seminar2006 sosbuch summerschool http://www.bibsonomy.org/bibtex/2d2b34ecaa23078ebef7a7ee84be509a4/hothohotho2009-02-24T20:37:08+01:00citation index network pagerank ranking toread <span class="authorEditorList"><a href="/author/Maslov">Sergei Maslov</a>, and <a href="/author/Redner">S. Redner</a> </span>(<em>2009</em>)<em>cite arxiv:0901.2640 Comment: 3 pages, 1 figure, invited comment for the Journal of Neuroscience. The arxiv version is microscopically different from t<span class="info">...<div>cite arxiv:0901.2640 Comment: 3 pages, 1 figure, invited comment for the Journal of Neuroscience. The arxiv version is microscopically different from the published version</div></span> . </em>Tue Feb 24 20:37:08 CET 2009cite arxiv:0901.2640 Comment: 3 pages, 1 figure, invited comment for the Journal of Neuroscience. The arxiv version is microscopically different from the published versionPromise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks2009citation index network pagerank ranking toread We review our recent work on applying the Google PageRank algorithm to find scientific &quot;gems&quot; among all Physical Review publications, and its extension to CiteRank, to find currently popular research directions. These metrics provide a meaningful extension to traditionally-used importance measures, such as the number of citations and journal impact factor. We also point out some pitfalls of over-relying on quantitative metrics to evaluate scientific quality. Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networkshttp://www.bibsonomy.org/bibtex/22a219a2664c566b405420f720583643a/hothohotho2009-01-16T11:50:31+01:00*** citeulike clustering dataset folksonomy network properties <span class="authorEditorList"><a href="/author/Capocci">Andrea Capocci</a>, and <a href="/author/Caldarelli">Guido Caldarelli</a> </span><em>Journal of Physics A: Mathematical and Theoretical</em> <em>41(22):224016 7pp</em> (<em>2008</em>)Fri Jan 16 11:50:31 CET 2009Journal of Physics A: Mathematical and Theoretical22224016 (7pp)Folksonomies and clustering in the collaborative system CiteULike412008*** citeulike clustering dataset folksonomy network properties We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.http://www.bibsonomy.org/bibtex/28dd63b723996dfa3fdff4fcfb9e3ce2e/hothohotho2008-11-21T14:12:46+01:00clustering graph network toread <span class="authorEditorList"><a href="/author/Xu">Xiaowei Xu</a>, <a href="/author/Yuruk">Nurcan Yuruk</a>, <a href="/author/Feng">Zhidan Feng</a>, and <a href="/author/Schweiger">Thomas A. J. Schweiger</a> </span><em>KDD &#039;07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page 824--833. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Fri Nov 21 14:12:46 CET 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining824--833SCAN: a structural clustering algorithm for networks2007clustering graph network toread SCANhttp://www.bibsonomy.org/bibtex/2dd7cd33e8a95a0128fe05adc46483ac7/hothohotho2008-08-28T10:19:29+02:00model network toread <span class="authorEditorList"><a href="/author/White">Douglas R. White</a>, <a href="/author/Kejzar">Natasa Kejzar</a>, <a href="/author/Tsallis">Constantino Tsallis</a>, <a href="/author/Farmer">Doyne Farmer</a>, and <a href="/author/White">Scott White</a> </span>(<em>2005</em>)Thu Aug 28 10:19:29 CEST 2008A generative model for feedback networks2005model network toread We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.[cond-mat/0508028] A generative model for feedback networkshttp://www.bibsonomy.org/bibtex/2f15cc7613101babb2c3ed1927e35213a/hothohotho2008-05-21T10:23:17+02:002007 folksonomies kdubiq myown network properties sosbuch summerschool <span class="authorEditorList"><a href="/author/Cattuto">Ciro Cattuto</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Baldassarri">Andrea Baldassarri</a>, <a href="/author/Servedio">Vito D. P. Servedio</a>, <a href="/author/Loreto">Vittorio Loreto</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Grahl">Miranda Grahl</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>AI Communications</em> <em>20(4):245 - 262</em> (<em>2007</em>)Wed May 21 10:23:17 CEST 2008AI Communications4245 - 262Network Properties of Folksonomies2020072007 folksonomies kdubiq myown network properties sosbuch summerschool http://www.bibsonomy.org/bibtex/27a080f640fa62fc81e73b9fab1e7447c/hothohotho2008-04-26T13:11:10+02:00digg dynamics flickr network social toread <span class="authorEditorList"><a href="/author/Lerman">Kristina Lerman</a> </span><em>arXiv</em> (<em>January 2007</em>)Sat Apr 26 13:11:10 CEST 2008arXivJanSocial Information Processing in Social News Aggregation2007digg dynamics flickr network social toread The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg.March 2008http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/hothohotho2008-04-05T17:54:56+02:002008 analysis folksonomy icwsm implicit log logsonomy myown network query search <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social MediaICWSM 2008, </em><em>AAAI Press, </em>(<em>2008</em>)Sat Apr 05 17:54:56 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy20082008 analysis folksonomy icwsm implicit log logsonomy myown network query search In social bookmarking 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. Search engines filter the vast information of the web. Queries 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. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.