@inproceedings{benevenuto2009characterizing,
  abstract = {Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.},
  acmid = {1644900},
  address = {New York, NY, USA},
  author = {Benevenuto, Fabr\'{\i}cio and Rodrigues, Tiago and Cha, Meeyoung and Almeida, Virg\'{\i}lio},
  booktitle = {Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference},
  doi = {10.1145/1644893.1644900},
  interhash = {ed9b10d4f36f90ddde9b95ce45b0b0be},
  intrahash = {e5e25244e1ca2316a7871727e2df2bb9},
  isbn = {978-1-60558-771-4},
  location = {Chicago, Illinois, USA},
  numpages = {14},
  pages = {49--62},
  publisher = {ACM},
  series = {IMC '09},
  title = {Characterizing User Behavior in Online Social Networks},
  url = {http://doi.acm.org/10.1145/1644893.1644900},
  year = 2009
}

@article{batagelj2011algorithms,
  abstract = {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 “},
  author = {Batagelj, Vladimir and Zaveršnik, Matjaž},
  doi = {10.1007/s11634-010-0079-y},
  interhash = {a0bd7331f81bb4da72ce115d5943d6e4},
  intrahash = {cd0d5266688af6bb98bde7f99e3a54c1},
  issn = {1862-5347},
  journal = {Advances in Data Analysis and Classification},
  language = {English},
  number = 2,
  pages = {129--145},
  publisher = {Springer},
  title = {Fast algorithms for determining (generalized) core groups in social networks},
  url = {http://dx.doi.org/10.1007/s11634-010-0079-y},
  volume = 5,
  year = 2011
}

@article{barabsi2013network,
  abstract = {Professor Barabási's talk described how the tools of network science can help understand the Web's structure, development and weaknesses. The Web is an information network, in which the nodes are documents (at the time of writing over one trillion of them), connected by links. Other well-known network structures include the Internet, a physical network where the nodes are routers and the links are physical connections, and organizations, where the nodes are people and the links represent communications.},
  author = {Barabási, Albert-László},
  doi = {10.1098/rsta.2012.0375},
  eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120375.full.pdf+html},
  interhash = {e2cfdd2e3c7c68581e3ab691909ed28b},
  intrahash = {208c1f9d6d8eff67cee07ebdf3cd0fc1},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  number = 1987,
  title = {Network science},
  url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120375.abstract},
  volume = 371,
  year = 2013
}

@article{kleinberg2013analysis,
  abstract = {The growth of the Web has required us to think about the design of information systems in which large-scale computational and social feedback effects are simultaneously at work. At the same time, the data generated by Web-scale systems—recording the ways in which millions of participants create content, link information, form groups and communicate with one another—have made it possible to evaluate long-standing theories of social interaction, and to formulate new theories based on what we observe. These developments have created a new level of interaction between computing and the social sciences, enriching the perspectives of both of these disciplines. We discuss some of the observations, theories and conclusions that have grown from the study of Web-scale social interaction, focusing on issues including the mechanisms by which people join groups, the ways in which different groups are linked together in social networks and the interplay of positive and negative interactions in these networks.},
  author = {Kleinberg, Jon},
  doi = {10.1098/rsta.2012.0378},
  eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120378.full.pdf+html},
  interhash = {b4686f01da53c975f342dbb40bdd1a90},
  intrahash = {e3898cfb7206a7fee8eb3a5419aa030f},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  month = mar,
  number = 1987,
  title = {Analysis of large-scale social and information networks},
  url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120378.abstract},
  volume = 371,
  year = 2013
}

@inproceedings{pfaltz2012entropy,
  abstract = {We introduce the concepts of closed sets and closure operators as mathematical tools for the study of social networks. Dynamic networks are represented by transformations. It is shown that under continuous change/transformation, all networks tend to "break down" and become less complex. It is a kind of entropy. The product of this theoretical decomposition is an abundance of triadically closed clusters which sociologists have observed in practice. This gives credence to the relevance of this kind of mathematical analysis in the sociological context. },
  author = {Pfaltz, John L.},
  booktitle = {Proceedings of the SOCINFO},
  interhash = {753f13a5ffaa0946220164c2b05c230f},
  intrahash = {044d0b1f6e737bede270a40bbddb0b06},
  title = {Entropy in Social Networks},
  year = 2012
}

@article{pham2011development,
  abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.},
  address = {Wien},
  affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany},
  author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias},
  doi = {10.1007/s13278-011-0024-x},
  interhash = {193312234ed176aa8be9f35d4d1c4e72},
  intrahash = {8ae08cacda75da80bfa5604cfce48449},
  issn = {1869-5450},
  journal = {Social Network Analysis and Mining},
  keyword = {Computer Science},
  number = 4,
  pages = {321--340},
  publisher = {Springer},
  title = {Development of computer science disciplines: a social network analysis approach},
  url = {http://dx.doi.org/10.1007/s13278-011-0024-x},
  volume = 1,
  year = 2011
}

@article{pham2011development,
  abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.},
  address = {Wien},
  affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany},
  author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias},
  doi = {10.1007/s13278-011-0024-x},
  interhash = {193312234ed176aa8be9f35d4d1c4e72},
  intrahash = {8ae08cacda75da80bfa5604cfce48449},
  issn = {1869-5450},
  journal = {Social Network Analysis and Mining},
  keyword = {Computer Science},
  number = 4,
  pages = {321--340},
  publisher = {Springer},
  title = {Development of computer science disciplines: a social network analysis approach},
  url = {http://dx.doi.org/10.1007/s13278-011-0024-x},
  volume = 1,
  year = 2011
}

@article{Ross2009578,
  abstract = {Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered.},
  author = {Ross, Craig and Orr, Emily S. and Sisic, Mia and Arseneault, Jaime M. and Simmering, Mary G. and Orr, R. Robert},
  doi = {10.1016/j.chb.2008.12.024},
  interhash = {98f45e3865f7e4802258347ee91e678b},
  intrahash = {fbcbb77a298da03d1e2d8bcd9ac3e0b4},
  issn = {0747-5632},
  journal = {Computers in Human Behavior},
  note = {<ce:title>Including the Special Issue: State of the Art Research into Cognitive Load Theory</ce:title>},
  number = 2,
  pages = {578 - 586},
  title = {Personality and motivations associated with Facebook use},
  url = {http://www.sciencedirect.com/science/article/pii/S0747563208002355},
  volume = 25,
  year = 2009
}

@incollection{koschtzki2005centrality,
  abstract = {Centrality indices are to quantify an intuitive feeling that in most networks some vertices or edges are more central than others. Many vertex centrality indices were introduced for the first time in the 1950s: e.g., the Bavelas index [50, 51], degree centrality [483] or a first feedback centrality, introduced by Seeley [510]. These early centralities raised a rush of research in which manifold applications were found. However, not every centrality index was suitable to every application, so with time, dozens of new centrality indices were published. This chapter will present some of the more influential, ‘classic’ centrality indices. We do not strive for completeness, but hope to give a catalog of basic centrality indices with some of their main applications.},
  address = {Berlin / Heidelberg},
  affiliation = {IPK Gatersleben, Corrensstraße 3, 06466 Gatersleben, Germany},
  author = {Koschützki, Dirk and Lehmann, Katharina and Peeters, Leon and Richter, Stefan and Tenfelde-Podehl, Dagmar and Zlotowski, Oliver},
  booktitle = {Network Analysis},
  doi = {10.1007/978-3-540-31955-9_3},
  editor = {Brandes, Ulrik and Erlebach, Thomas},
  interhash = {8bfa60518049d9dbc7f6ce7b5c2914be},
  intrahash = {567d2f61b08e78af53463b2a30729830},
  isbn = {978-3-540-24979-5},
  keyword = {Computer Science},
  pages = {16-61},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Centrality Indices},
  url = {http://dx.doi.org/10.1007/978-3-540-31955-9_3},
  volume = 3418,
  year = 2005
}

@book{brandes2005network,
  address = {[New York]},
  author = {Brandes, Ulrik. and Erlebach, Thomas.},
  interhash = {ae40403faa9a80926c66da73cf6e29ba},
  intrahash = {11695c81746f2ac6e25fab7c6ed49fbf},
  isbn = {9783540249795 3540249796 9783540319559 3540319557},
  publisher = {Springer-Verlag Berlin/Heidelberg},
  refid = {318289062},
  title = {Network Analysis},
  url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=3540249796},
  year = 2005
}

@article{gansner2009drawing,
  abstract = {Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through  dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap: a practical tool for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains All the maps referenced in this paper can be found in http://www.research.att.com/~yifanhu/GMap },
  author = {Gansner, Emden R. and Hu, Yifan and Kobourov, Stephen G.},
  interhash = {881280a1a2aa34d84322d3781f62ca90},
  intrahash = {3f9e522da9443c0a07c39009918a4a77},
  journal = {cs.CG},
  month = jul,
  title = {{GMap}: Drawing Graphs as Maps},
  url = {http://arxiv.org/abs/0907.2585},
  volume = {arXiv:0907.2585v1},
  year = 2009
}

@proceedings{stumme05semanticnetworkanalysis,
  address = {Aachen},
  editor = {Stumme, Gerd and Hoser, Bettina and Schmitz, Christoph and Alani, Harith},
  interhash = {6316cb226778a6a6f156821f975b2ba3},
  intrahash = {c44763991d44182c53606a2c93054f26},
  issn = {1613-0073},
  publisher = {CEUR Proceedings},
  title = {Proceedings of the First Workshop on Semantic Network Analysis },
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/},
  year = 2005
}

@article{barrat2004architecture,
  abstract = {Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.},
  author = {Barrat, A. and Barthélemy, M. and Pastor-Satorras, R. and Vespignani, A.},
  doi = {10.1073/pnas.0400087101},
  eprint = {http://www.pnas.org/content/101/11/3747.full.pdf+html},
  interhash = {23fefd4f4ae5efca8661daa7585100dd},
  intrahash = {a326012bd3b8dd805aafaa79e7d5742b},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  number = 11,
  pages = {3747--3752},
  title = {The architecture of complex weighted networks},
  url = {http://www.pnas.org/content/101/11/3747.abstract},
  volume = 101,
  year = 2004
}

@article{vazquez2006modeling,
  abstract = { The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queuing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution P(τw)∼τw−α with α=3∕2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by α=1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display α=1, the surface mail based communication belongs to the α=3∕2 universality class. Finally, we discuss possible extension of the proposed queuing models and outline some future challenges in exploring the statistical mechanics of human dynamics.},
  author = {Vázquez, Alexei and Gama Oliveira, João and Dezsö, Zoltán and Goh, Kwang-Il and Kondor, Imre and Barabási, Albert-László},
  doi = {10.1103/PhysRevE.73.036127},
  interhash = {679487e36d59d3d8262632b9a05f9f45},
  intrahash = {f15dafcb20d0c9857acf1324c5c2279c},
  journal = {Physical Review E},
  month = mar,
  number = 3,
  numpages = {19},
  pages = 036127,
  publisher = {American Physical Society},
  title = {Modeling bursts and heavy tails in human dynamics},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.73.036127},
  volume = 73,
  year = 2006
}

@incollection{hoser2006semantic,
  abstract = { A key argument for modeling knowledge in ontologies is the easy reuse 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. },
  address = {Berlin/Heidelberg},
  author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  doi = {10.1007/11762256_38},
  editor = {Sure, York and Domingue, John},
  interhash = {344ec3b4ee8af1a2c6b86efc14917fa9},
  intrahash = {2b720233e4493d4e0dee95be86dd07e8},
  isbn = {978-3-540-34544-2},
  note = {10.1007/11762256_38},
  pages = {514--529},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Semantic Network Analysis of Ontologies},
  url = {http://dx.doi.org/10.1007/11762256_38},
  volume = 4011,
  year = 2006
}

@article{cattuto2007network,
  abstract = {Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures - so-called folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them.Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.},
  address = {Amsterdam, The Netherlands},
  author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd},
  file = {cattuto2007network.pdf:cattuto2007network.pdf:PDF},
  groups = {public},
  interhash = {fc5f2df61d28bc99b7e15029da125588},
  intrahash = {1dfe8b2aa29adf4929cbb845950f78bc},
  issn = {0921-7126},
  journal = {AI Communications},
  journalpub = {1},
  month = dec,
  number = 4,
  pages = {245--262},
  publisher = {IOS Press},
  timestamp = {2010-11-10 15:35:25},
  title = {Network properties of folksonomies},
  url = {http://portal.acm.org/citation.cfm?id=1365538},
  username = {dbenz},
  volume = 20,
  year = 2007
}

@incollection{lerner2005assignments,
  abstract = {9.0. 9.0.1. Preliminaries 9.0.2. Role Graph 9.1. Structural Equivalence 9.1.1. Lattice of Equivalence Relations 9.1.2. Lattice of Structural Equivalences 9.1.3. Computation of Structural Equivalences 9.2. Regular Equivalence 9.2.1. Elementary Properties 9.2.2. Lattice Structure and Regular Interior 9.2.3. Computation of Regular Interior 9.2.4. The Role Assignment Problem 9.2.5. Existence of k-Role Assignments 9.3. Other Equivalences 9.3.1. Exact Role Assignments 9.3.2. Automorphic and Orbit Equivalence 9.3.3. Perfect Equivalence 9.3.4. Relative Regular Equivalence 9.4. Graphs with Multiple Relations 9.5. The Semigroup of a Graph 9.5.1. Winship-Pattison Role Equivalence 9.6. Chapter Notes},
  address = {Berlin / Heidelberg},
  affiliation = {Computer &amp; Information Science, University of Konstanz, Box D 67, 78457 Konstanz Germany},
  author = {Lerner, Jürgen},
  booktitle = {Network Analysis},
  doi = {10.1007/978-3-540-31955-9_9},
  editor = {Brandes, Ulrik and Erlebach, Thomas},
  interhash = {59200f990b15e99c7ab3df74fcb8443e},
  intrahash = {7385d25825c7692ffdc9b12b0ed85989},
  pages = {216-252},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Role Assignments},
  url = {http://dx.doi.org/10.1007/978-3-540-31955-9_9},
  volume = 3418,
  year = 2005
}

@article{cattuto2007network,
  abstract = {Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures - so-called folksonomies - as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them.  Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam. },
  address = {Amsterdam, The Netherlands},
  author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd},
  interhash = {fc5f2df61d28bc99b7e15029da125588},
  intrahash = {f15cc7613101babb2c3ed1927e35213a},
  issn = {0921-7126},
  journal = {AI Communications},
  month = dec,
  number = 4,
  pages = {245--262},
  publisher = {IOS Press},
  title = {Network Properties of Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2007network.pdf},
  volume = 20,
  year = 2007
}

@inproceedings{krause2008logsonomy,
  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.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia},
  doi = {10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {e64d14f3207766f4afc65983fa759ffe},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - Social Information Retrieval with Logdata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/krause2008logsonomy.pdf},
  vgwort = {17},
  year = 2008
}

@inproceedings{scripps2007roles,
  abstract = {A node role is a subjective characterization of the part it plays in a network structure. Knowing the role of a node is important for many link mining applications. For example, in Web search, nodes that are deemed to be authorities on a given topic are often found to be most relevant to the user's queries. There are a number of metrics that can be used to assign roles to individual nodes in a network, including degree, closeness, and betweenness. None of these metrics, however, take into account the community structure that underlies the network. In this paper we define community-based roles that the nodes can assume (ambassadors, big fish, loners, and bridges) and show how existing link mining techniques can be improved by knowledge of such roles. A new community-based metric is introduced for estimating the number of communities linked to a node. Using this metric and a modification of degree, we show how to assign community-based roles to the nodes. We also illustrate the benefits of knowing the community-based node roles in the context of link-based classification and influence maximization.},
  acmid = {1348553},
  address = {New York, NY, USA},
  author = {Scripps, Jerry and Tan, Pang-Ning and Esfahanian, Abdol-Hossein},
  booktitle = {Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis},
  doi = {http://doi.acm.org/10.1145/1348549.1348553},
  interhash = {bc67321ee8dc1e7db1c3c234833a5519},
  intrahash = {4435192c25bfc86b47f030011f4ce1ef},
  isbn = {978-1-59593-848-0},
  location = {San Jose, California},
  numpages = {10},
  pages = {26--35},
  publisher = {ACM},
  series = {WebKDD/SNA-KDD '07},
  title = {Node roles and community structure in networks},
  url = {http://doi.acm.org/10.1145/1348549.1348553},
  year = 2007
}