@inproceedings{angelova2008characterizing,
  abstract = {Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution. },
  author = {Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł},
  booktitle = {Proceedings of the Mining Social Data Workshop (MSoDa)},
  interhash = {f74d27a66d2754f3d5892d68c4abee4c},
  intrahash = {02d6739886a13180dd92fbb7243ab58b},
  month = jul,
  organization = {ECAI 2008},
  pages = {21--25},
  title = {Characterizing a social bookmarking and tagging network},
  url = {http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf},
  year = 2008
}

@inproceedings{krafft2010enabling,
  abstract = {The VIVO project is creating an open, Semantic Web-based network of institutional ontology-driven databases to enable national discovery, networking, and collaboration via information sharing about researchers and their activities. The project has been funded by NIH to implement VIVO at the University of Florida, Cornell University, and Indiana University Bloomington together with four other partner institutions. Working with the Semantic Web/Linked Open Data community, the project will pilot the development of common ontologies, integration with institutional information sources and authentication, and national discovery and exploration of networks of researchers.  Building on technology developed over the last five years at Cornell University, VIVO supports the flexible description and interrelation of people, organizations, activities, projects, publications, affiliations, and other entities and properties. VIVO itself is an open source Java application built on W3C Semantic Web standards, including RDF, OWL, and SPARQL. To create researcher profiles, VIVO draws on authoritative information from institutional databases, external data sources such as PubMed, and information provided directly by researchers themselves. While the NIH-funded project focuses on biomedical research, the current Cornell implementation of VIVO supports the full range of disciplines across the university, from music to mechanical engineering to management.  There are many ways a person?s expertise may be discovered, through grants, presentations, courses and news releases, as well as through research statements or publications listed on their profile{--}resulting in the creation of implicit groups or networks of people based on a number of pre-identified, shared characteristics. In addition to formal authoritative information and relationships, VIVO can also support the creation of personal work groups and associated properties to represent the informal relationships evolving around collaboration.},
  author = {Krafft, Dean B. and Cappadona, Nicholas A. and Caruso, Brian and Corson-Rikert, Jon and Devare, Medha and Lowe, Brian J. and Collaboration, VIVO},
  booktitle = {WebSci10: Extending the Frontiers of Society On-Line},
  interhash = {be9a22c8b28fcf00dc26025b5b127956},
  intrahash = {87a568555fcc35532e9384337c1ce68a},
  title = {VIVO: Enabling National Networking of Scientists},
  url = {http://journal.webscience.org/316/},
  year = 2010
}

@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
}

@inproceedings{zesch2007analysis,
  abstract = {In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms. },
  address = {Rochester},
  author = {Zesch, Torsten and Gurevych, Iryna},
  booktitle = {Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)},
  interhash = {0401e62edb9bfa85dd498cb40301c0cb},
  intrahash = {332ed720a72bf069275f93485432314b},
  month = apr,
  pages = {1--8},
  publisher = {Association for Computational Linguistics},
  title = {Analysis of the Wikipedia Category Graph for NLP Applications},
  url = {http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11},
  year = 2007
}

@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{birkholz2012scalable,
  abstract = {Studies on social networks have proved that endogenous and exogenous factors influence dynamics. Two streams of modeling exist on explaining the dynamics of social networks: 1) models predicting links through network properties, and 2) models considering the effects of social attributes. In this interdisciplinary study we work to overcome a number of computational limitations within these current models. We employ a mean-field model which allows for the construction of a population-specific socially informed model for predicting links from both network and social properties in large social networks. The model is tested on a population of conference coauthorship behavior, considering a number of parameters from available Web data. We address how large social networks can be modeled preserving both network and social parameters. We prove that the mean-field model, using a data-aware approach, allows us to overcome computational burdens and thus scalability issues in modeling large social networks in terms of both network and social parameters. Additionally, we confirm that large social networks evolve through both network and social-selection decisions; asserting that the dynamics of networks cannot singly be studied from a single perspective but must consider effects of social parameters. },
  author = {Birkholz, Julie M. and Bakhshi, Rena and Harige, Ravindra and van Steen, Maarten and Groenewegen, Peter},
  interhash = {a8ef0aac2eab74fc8eb3f9d3dc8a32dd},
  intrahash = {aefcc2aa922b048bec85d5070494ed81},
  journal = {CoRR},
  month = sep,
  title = {Scalable Analysis of Socially Informed Network Models: the data-aware mean-field approach },
  url = {http://arxiv.org/abs/1209.6615},
  volume = {abs/1209.6615},
  year = 2012
}

@article{kautz1997referral,
  acmid = {245123},
  address = {New York, NY, USA},
  author = {Kautz, Henry and Selman, Bart and Shah, Mehul},
  doi = {10.1145/245108.245123},
  interhash = {6995678b936b33eef9ea1396e53a1fc7},
  intrahash = {832d16a8c86e769c7ac9ace5381f757e},
  issn = {0001-0782},
  issue_date = {March 1997},
  journal = {Communications of the ACM},
  month = mar,
  number = 3,
  numpages = {3},
  pages = {63--65},
  publisher = {ACM},
  title = {Referral Web: combining social networks and collaborative filtering},
  url = {http://doi.acm.org/10.1145/245108.245123},
  volume = 40,
  year = 1997
}

@incollection{yu2000social,
  abstract = {Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.},
  address = {Berlin/Heidelberg},
  affiliation = {Department of Computer Science, North Carolina State University, Raleigh, NC 27695-7534, USA},
  author = {Yu, Bin and Singh, Munindar},
  booktitle = {Cooperative Information Agents IV - The Future of Information Agents in Cyberspace},
  doi = {10.1007/978-3-540-45012-2_15},
  editor = {Klusch, Matthias and Kerschberg, Larry},
  interhash = {1065a4963600ef4f9b4c034d3bbd9a50},
  intrahash = {337afcb67138b927b27a9687199e8568},
  isbn = {978-3-540-67703-1},
  keyword = {Computer Science},
  pages = {355--393},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Social Mechanism of Reputation Management in Electronic Communities},
  url = {http://dx.doi.org/10.1007/978-3-540-45012-2_15},
  volume = 1860,
  year = 2000
}