@inproceedings{DBLP:conf/dsaa/KrompassNT14,
  author = {Krompass, Denis and Nickel, Maximilian and Tresp, Volker},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA}               2014, Shanghai, China, October 30 - November 1, 2014},
  crossref = {DBLP:conf/dsaa/2014},
  doi = {10.1109/DSAA.2014.7058046},
  interhash = {0ca986606c22ca0b3780c9b9c25f31c7},
  intrahash = {c952ed96ece470e4fa5336eedf670d5b},
  isbn = {978-1-4799-6991-3},
  pages = {18--24},
  publisher = {{IEEE}},
  title = {Large-scale factorization of type-constrained multi-relational data},
  url = {http://dx.doi.org/10.1109/DSAA.2014.7058046},
  year = 2014
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@misc{ugander2011anatomy,
  abstract = {We study the structure of the social graph of active Facebook users, the
largest social network ever analyzed. We compute numerous features of the graph
including the number of users and friendships, the degree distribution, path
lengths, clustering, and mixing patterns. Our results center around three main
observations. First, we characterize the global structure of the graph,
determining that the social network is nearly fully connected, with 99.91% of
individuals belonging to a single large connected component, and we confirm the
"six degrees of separation" phenomenon on a global scale. Second, by studying
the average local clustering coefficient and degeneracy of graph neighborhoods,
we show that while the Facebook graph as a whole is clearly sparse, the graph
neighborhoods of users contain surprisingly dense structure. Third, we
characterize the assortativity patterns present in the graph by studying the
basic demographic and network properties of users. We observe clear degree
assortativity and characterize the extent to which "your friends have more
friends than you". Furthermore, we observe a strong effect of age on friendship
preferences as well as a globally modular community structure driven by
nationality, but we do not find any strong gender homophily. We compare our
results with those from smaller social networks and find mostly, but not
entirely, agreement on common structural network characteristics.},
  author = {Ugander, Johan and Karrer, Brian and Backstrom, Lars and Marlow, Cameron},
  interhash = {968abebf69b5959d2837eefcda3a8a32},
  intrahash = {efad3d029704f09829373a443eeefdde},
  note = {cite arxiv:1111.4503Comment: 17 pages, 9 figures, 1 table},
  title = {The Anatomy of the Facebook Social Graph},
  url = {http://arxiv.org/abs/1111.4503},
  year = 2011
}

@article{liu2012fulltext,
  author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun},
  interhash = {011df26355ad51a88947017fd2791a98},
  intrahash = {f9c6133bf4503003822f99860f864698},
  journal = {Journal of the American Society for Information Science and Technology},
  title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network},
  url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf},
  year = 2012
}

@misc{Leskovec2010,
  abstract = {  Detecting clusters or communities in large real-world graphs such as large
social or information networks is a problem of considerable interest. In
practice, one typically chooses an objective function that captures the
intuition of a network cluster as set of nodes with better internal
connectivity than external connectivity, and then one applies approximation
algorithms or heuristics to extract sets of nodes that are related to the
objective function and that "look like" good communities for the application of
interest. In this paper, we explore a range of network community detection
methods in order to compare them and to understand their relative performance
and the systematic biases in the clusters they identify. We evaluate several
common objective functions that are used to formalize the notion of a network
community, and we examine several different classes of approximation algorithms
that aim to optimize such objective functions. In addition, rather than simply
fixing an objective and asking for an approximation to the best cluster of any
size, we consider a size-resolved version of the optimization problem.
Considering community quality as a function of its size provides a much finer
lens with which to examine community detection algorithms, since objective
functions and approximation algorithms often have non-obvious size-dependent
behavior.
},
  author = {Leskovec, Jure and Lang, Kevin J. and Mahoney, Michael W.},
  interhash = {0e58de655596b2198f4a7001facd0c32},
  intrahash = {410a9cbea51ea5dd3c56aad26a0e11b2},
  note = {cite arxiv:1004.3539
},
  title = {Empirical Comparison of Algorithms for Network Community Detection},
  url = {http://arxiv.org/abs/1004.3539},
  year = 2010
}

@misc{noauthororeditorpipeline,
  author = {Namata, Galileo Mark and Getoor, Lise},
  booktitle = {7 th International Workshop on Mining and Learning with Graphs},
  interhash = {c341a683d8bac1896a962d8907284b3d},
  intrahash = {88f20464c94d29ad9d2f2cd9ba82d3f9},
  title = {A Pipeline Approach to Graph Identication },
  url = {http://linqs.cs.umd.edu/basilic/web/Publications/},
  year = 2009
}

@inproceedings{conf/ht/WuZM06,
  author = {Wu, Harris and Zubair, Mohammad and Maly, Kurt},
  booktitle = {Hypertext},
  crossref = {conf/ht/2006},
  date = {2006-09-28},
  editor = {Wiil, Uffe Kock and Nürnberg, Peter J. and Rubart, Jessica},
  ee = {http://doi.acm.org/10.1145/1149941.1149962},
  interhash = {ea6aa5db3724812d08347d5a8309bea4},
  intrahash = {4b0512091911843390f88699d3ea3bb9},
  isbn = {1-59593-417-0},
  pages = {111-114},
  publisher = {ACM},
  title = {Harvesting social knowledge from folksonomies.},
  url = {http://dblp.uni-trier.de/db/conf/ht/ht2006.html#WuZM06},
  year = 2006
}

@inproceedings{1281280,
  address = {New York, NY, USA},
  author = {Xu, Xiaowei and Yuruk, Nurcan and Feng, Zhidan and Schweiger, Thomas A. J.},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281280},
  interhash = {cff0749eaf202838fd393faa1f1ea0af},
  intrahash = {8dd63b723996dfa3fdff4fcfb9e3ce2e},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {824--833},
  publisher = {ACM},
  title = {SCAN: a structural clustering algorithm for networks},
  url = {http://portal.acm.org/citation.cfm?doid=1281192.1281280},
  year = 2007
}

@article{chen:058701,
  author = {Chen, Yiping and Paul, Gerald and Havlin, Shlomo and Liljeros, Fredrik and Stanley, H. Eugene},
  doi = {10.1103/PhysRevLett.101.058701},
  eid = {058701},
  interhash = {591effe237db9e7b8443c05390e5a6f4},
  intrahash = {3409d4e03990b0ff2a9704b665adf16e},
  journal = {Physical Review Letters},
  number = 5,
  numpages = {4},
  pages = 058701,
  publisher = {APS},
  title = {Finding a Better Immunization Strategy},
  url = {http://link.aps.org/abstract/PRL/v101/e058701},
  volume = 101,
  year = 2008
}

@inproceedings{1102357,
  address = {New York, NY, USA},
  author = {Bekkerman, Ron and El-Yaniv, Ran and McCallum, Andrew},
  booktitle = {ICML '05: Proceedings of the 22nd international conference on Machine learning},
  doi = {http://doi.acm.org/10.1145/1102351.1102357},
  interhash = {25609f84a6916c1664e61d8618f46a32},
  intrahash = {a5ac489feb7407a07570f6733665a6dd},
  isbn = {1-59593-180-5},
  location = {Bonn, Germany},
  pages = {41--48},
  publisher = {ACM Press},
  title = {Multi-way distributional clustering via pairwise interactions},
  url = {http://www.cs.technion.ac.il/~rani/el-yaniv-papers/BekkermanEM05.pdf},
  year = 2005
}

@inproceedings{1281269,
  address = {New York, NY, USA},
  author = {Tantipathananandh, Chayant and Berger-Wolf, Tanya and Kempe, David},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281269},
  interhash = {9373b48866b4faa1941db0bee9265af0},
  intrahash = {27a4fb58300979d4dbe94e75422418bd},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {717--726},
  publisher = {ACM},
  title = {A framework for community identification in dynamic social networks},
  url = {http://portal.acm.org/citation.cfm?doid=1281192.1281269},
  year = 2007
}

@inproceedings{Chakrabarti:2004,
  author = {Chakrabarti, D. and Zhan, Y. and Faloutsos, C.},
  booktitle = {SIAM International Conference on Data Mining},
  interhash = {d7719c6e919fbb8a37e09464f12988b6},
  intrahash = {5e5cc221d7da719909f3bf8c507b0afc},
  title = {R-MAT: A Recursive Model for Graph Mining},
  url = {http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf},
  year = 2004
}

@inproceedings{schmitz2006content,
  address = {Budva, Montenegro},
  author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  interhash = {940fa3c671c771cc9a644b3ecfef43cd},
  intrahash = {9a06428ec3bd72e3ea6c7a8f08e2bb85},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {530-544},
  publisher = {Springer},
  series = {LNCS},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf},
  vgwort = {27},
  volume = 4011,
  year = 2006
}

@inproceedings{conf/ACMse/BalakrishnanD06,
  author = {Balakrishnan, Hemant and Deo, Narsingh},
  booktitle = {ACM Southeast Regional Conference},
  crossref = {conf/ACMse/2006},
  date = {2006-12-18},
  editor = {Menezes, Ronaldo},
  ee = {http://doi.acm.org/10.1145/1185448.1185512},
  interhash = {e5777f1470aec341ee766ab04febb69a},
  intrahash = {7efa21cb8537359f6995cde9c307d181},
  isbn = {1-59593-315-8},
  pages = {280-285},
  publisher = {ACM},
  title = {Discovering communities in complex networks.},
  url = {http://www.cs.ucf.edu/csdept/faculty/deo/ACMSE-06.pdf},
  year = 2006
}

@misc{gartner02kernels,
  author = {Gartner, T. and Lloyd, J. and Flach, P.},
  interhash = {c30ebe2ea3894f6adf789c193e09aa8a},
  intrahash = {279dc9289b7b766d7ab316ffab9b3c05},
  text = {T. Gartner, J. W. Lloyd, and P. A. Flach. Kernels for structured data.
    In Proceedings of the 12th International Conference on Inductive Logic Programming.
    Springer-Verlag, 2002.},
  title = {Kernels for structured data},
  url = {http://citeseer.ist.psu.edu/609396.html},
  year = 2002
}

@inproceedings{yon2007,
  abstract = {The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying {\em communities} in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100\% for communities of a thirty nodes or more (even at low density). It is still about 80\% even for communities of twenty nodes with density over $50\%$ of the arcs present. At the lower extremes the algorithm catches 35\% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.},
  author = {Dourisboure, Yon and Geraci, Filippo and Pellegrini, Marco},
  booktitle = {Proc of the wwww},
  interhash = {a07d927ef48ae1b8f3338541857c5a34},
  intrahash = {480a63c3e6847dc8a9ebd3de040501db},
  title = {Extraction and Classification of Dense Communities in the WebAuthors},
  url = {http://www2007.org/program/paper.php?id=15},
  year = 2007
}

@article{10.1109/WI.2006.202,
  address = {Los Alamitos, CA, USA},
  author = {Lo, Shuchuan and Lin, Chingching},
  doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2006.202},
  interhash = {a253307885e2fd3480df51d0c982e7c2},
  intrahash = {953fb16de9131ebda51011eb2f5e4c51},
  isbn = {0-7695-2747-7},
  journal = {wi},
  pages = {121-128},
  publisher = {IEEE Computer Society},
  title = {WMR--A Graph-Based Algorithm for Friend Recommendation},
  volume = 0,
  year = 2006
}

@article{citeulike:373702,
  author = {Huang, Zan and Chung, Wingyan and Ong, Thian-Huat and Chen, Hsinchun},
  citeulike-article-id = {373702},
  interhash = {e118610d6f67056a67adf54e6be84207},
  intrahash = {1bdb59e9985512349189d5b41691fd55},
  journal = {JCDL '02},
  pages = {65--73},
  priority = {3},
  title = {A Graph-based Recommender System for Digital Library},
  url = {http://delivery.acm.org/10.1145/550000/544231/p65-huang.pdf?key1=544231\&key2=8265870311\&coll=GUIDE\&dl=ACM\&CFID=56965103\&CFTOKEN=1581829},
  year = 2002
}

@inproceedings{conf/das/SchenkerBLK04,
  author = {Schenker, Adam and Bunke, Horst and Last, Mark and Kandel, Abraham},
  booktitle = {Document Analysis Systems},
  crossref = {conf/das/2004},
  date = {2005-01-05},
  editor = {Marinai, Simone and Dengel, Andreas},
  ee = {http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3163&spage=401},
  interhash = {83ba06e8918a227fb2345e047e40f619},
  intrahash = {4450261ce5af13db99ce208800dff22c},
  isbn = {3-540-23060-2},
  pages = {401-412},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {A Graph-Based Framework for Web Document Mining.},
  url = {http://dblp.uni-trier.de/db/conf/das/das2004.html#SchenkerBLK04},
  volume = 3163,
  year = 2004
}

@article{keyhere,
  asin = {9812563393},
  author = {Schenker, Adam and Bunke, Horst and Last, Mark and Kandel, Abraham},
  interhash = {247e95a6025dff9119c7943b5a33f917},
  intrahash = {3f9897fc8abcf1bcb1fd0212a23a4134},
  isbn = {9812563393},
  title = {Graph-Theoretic Techniques for Web Content Mining},
  typesource = {Simple CitationSource},
  url = {http://www.amazon.ca/Graph-Theoretic-Techniques-Web-Content-Mining/dp/9812563393/ref=sr_1_7/701-3503486-7337153?ie=UTF8&s=books&qid=1175673405&sr=1-7},
  year = 2005
}