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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Brandes, U., Delling, D., Gaertler, M., Görke, R., Hoefer, M., Nikoloski, Z. & Wagner, D. On Finding Graph Clusterings with Maximum Modularity 2007 Graph-Theoretic Concepts in Computer Science   incollection DOIURL  
Abstract: Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts, and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomaration approach.
BibTeX:
@incollection{springerlink:10.1007/978-3-540-74839-7_12,
  author = {Brandes, Ulrik and Delling, Daniel and Gaertler, Marco and Görke, Robert and Hoefer, Martin and Nikoloski, Zoran and Wagner, Dorothea},
  title = {On Finding Graph Clusterings with Maximum Modularity},
  booktitle = {Graph-Theoretic Concepts in Computer Science},
  publisher = {Springer},
  year = {2007},
  volume = {4769},
  pages = {121-132},
  url = {http://dx.doi.org/10.1007/978-3-540-74839-7_12},
  doi = {http://dx.doi.org/10.1007/978-3-540-74839-7_12}
}
Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Content Aggregation on Knowledge Bases using Graph Clustering 2006 The Semantic Web: Research and Applications   inproceedings URL  
Abstract: Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.
This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.
BibTeX:
@inproceedings{schmitz2006content,
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  booktitle = {The Semantic Web: Research and Applications},
  publisher = {Springer},
  year = {2006},
  volume = {4011},
  pages = {530-544},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf}
}
Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Content Aggregation on Knowledge Bases using Graph Clustering 2006 Proceedings of the 3rd European Semantic Web Conference   inproceedings URL  
BibTeX:
@inproceedings{schmitz2006content,
  author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  publisher = {Springer},
  year = {2006},
  volume = {4011},
  pages = {530-544},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf}
}
Schmitz, C., Hotho, A., Jäschke, R. & Stumme, G. Content Aggregation on Knowledge Bases using Graph Clustering 2006 The Semantic Web: Research and Applications   inproceedings URL  
Abstract: Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.

BibTeX:
@inproceedings{schmitz2006content,
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  booktitle = {The Semantic Web: Research and Applications},
  publisher = {Springer},
  year = {2006},
  volume = {4011},
  pages = {530-544},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf}
}
Dhillon, I. S., Mallela, S. & Modha, D. S. Information-Theoretic Co-Clustering 2003 Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2003)   inproceedings URL  
BibTeX:
@inproceedings{dhillon:mallela:modha:03,
  author = {Dhillon, I. S. and Mallela, S. and Modha, D. S.},
  title = {Information-Theoretic Co-Clustering},
  booktitle = {Proceedings of The Ninth ACM SIGKDD International              Conference on Knowledge Discovery and Data Mining({KDD}-2003)},
  year = {2003},
  pages = {89--98},
  url = {/brokenurl#citeseer.ist.psu.edu/dhillon03informationtheoretic.html}
}
Newman, M. E. J. The structure and function of complex networks 2003   misc URL  
Abstract: Inspired by empirical studies of networked systems such as the Internet,
cial networks, and biological networks, researchers have in recent years
veloped a variety of techniques and models to help us understand or predict
e behavior of these systems. Here we review developments in this field,
cluding such concepts as the small-world effect, degree distributions,
ustering, network correlations, random graph models, models of network growth
d preferential attachment, and dynamical processes taking place on networks.
BibTeX:
@misc{citeulike:155,
  author = {Newman, M. E. J.},
  title = {The structure and function of complex networks},
  year = {2003},
  url = {http://arxiv.org/abs/cond-mat/0303516}
}
Ng, A. Y., Jordan, M. I. & Weiss, Y. On spectral clustering: Analysis and an algorithm 2001 Advances in Neural Information Processing Systems 14   inproceedings  
Abstract: Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1
BibTeX:
@inproceedings{Ng01onspectral,
  author = {Ng, Andrew Y. and Jordan, Michael I. and Weiss, Yair},
  title = {On spectral clustering: Analysis and an algorithm},
  booktitle = {Advances in Neural Information Processing Systems 14},
  publisher = {MIT Press},
  year = {2001},
  pages = {849--856}
}
Ranade, A. Some uses of spectral methods 2000   unpublished  
BibTeX:
@unpublished{ranade:sus,
  author = {Ranade, A.G.},
  title = {{Some uses of spectral methods}},
  year = {2000}
}
Spielman, D. A. & Teng, S. Spectral Partitioning Works: Planar Graphs and Finite Element Meshes 1996   techreport  
BibTeX:
@techreport{Spielman:1996,
  author = {Spielman, Daniel A. and Teng, Shang},
  title = {Spectral Partitioning Works: Planar Graphs and Finite Element Meshes},
  publisher = {University of California at Berkeley},
  year = {1996}
}
Pothen, A., Simon, H. & Liou, K. Partitioning Sparse Matrices with Eigenvectors of Graphs 1990 SIAM J. MATRIX ANAL. APPLIC.   article URL  
BibTeX:
@article{partitioning89,
  author = {Pothen, A. and Simon, H.D. and Liou, K.P.},
  title = {{Partitioning Sparse Matrices with Eigenvectors of Graphs}},
  journal = {SIAM J. MATRIX ANAL. APPLIC.},
  year = {1990},
  volume = {11},
  number = {3},
  pages = {430--452},
  url = {http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf }
}
Donath, W. & Hoffman, A. Lower bounds for the partitioning of graphs 1973 IBM Journal of Research and Development   article  
BibTeX:
@article{donath1973lbp,
  author = {Donath, W.E. and Hoffman, A.J.},
  title = {{Lower bounds for the partitioning of graphs}},
  journal = {IBM Journal of Research and Development},
  year = {1973},
  volume = {17},
  number = {5},
  pages = {420--425}
}

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