ConferenceProceedingsinproceedingsNg01onspectralOn spectral clustering: Analysis and an algorithm2001NgY.AndrewJordanI.MichaelWeissYair849-856MIT PressAdvances in Neural Information Processing Systems 14Advances in Neural Information Processing Systems 14Despite 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. 1clustering, community, detection, graph, spectral, theoryJournalArticledonath1973lbp{Lower bounds for the partitioning of graphs}1973DonathW.E.HoffmanA.J.420-42517IBM Journal of Research and Development5clustering, community, detection, graph, spectral, theoryJournalArticlepartitioning89{Partitioning Sparse Matrices with Eigenvectors of Graphs}1990PothenA.SimonH.D.LiouK.P.430-45211SIAM J. MATRIX ANAL. APPLIC.3http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf clustering, community, graph, partitioning, spectral, theoryBookSectionincollectionspringerlink:10.1007/978-3-540-74839-7_12On Finding Graph Clusterings with Maximum Modularity2007BrandesUlrikDellingDanielGaertlerMarcoGörkeRobertHoeferMartinNikoloskiZoranWagnerDorotheaBrandstädtAndreasKratschDieterMüllerHaiko121-1324769 ISBN: 978-3-540-74838-0 DOI: 10.1007/978-3-540-74839-7_12SpringerBerlin / HeidelbergGraph-Theoretic Concepts in Computer ScienceGraph-Theoretic Concepts in Computer Sciencehttp://dx.doi.org/10.1007/978-3-540-74839-7_12Lecture Notes in Computer ScienceModularity 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.clustering, graph, modularity, theoryDepartment of Computer and Information Science, University of KonstanzReporttechreportSpielman:1996Spectral Partitioning Works: Planar Graphs and Finite Element Meshes1996SpielmanA.DanielTengShangUniversity of California at BerkeleyBerkeley, CA, USATech. rep.clustering, community, detection, graph, spectral, survey, theoryMiscciteulike:155The structure and function of complex networks2003NewmanE. J.M.Marchhttp://arxiv.org/abs/cond-mat/0303516The structure and function of complex networksInspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.algorithm, clustering, complex_systems, folksonomy, information, kdubiq, network, retrieval, scale_free_networks, small, socialnetwork, summerschool, theory, web, web_graph, worldConferenceProceedingsinproceedingsschmitz2006contentContent Aggregation on Knowledge Bases using Graph Clustering2006SchmitzChristophHothoAndreasJ\"aschkeRobertStummeGerd530-5444011 ISBN: 3-540-34544-2SpringerBudva, MontenegroProceedings of the 3rd European Semantic Web ConferenceProceedings of the 3rd European Semantic Web ConferenceJunehttp://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdfLNCS2006, aggregation, clustering, content, graph, myown, ontology, theoryConferenceProceedingsinproceedingsdhillon:mallela:modha:03Information-Theoretic Co-Clustering2003DhillonS.I.MallelaS.ModhaS.D.89-98Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining({KDD}-2003)Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining({KDD}-2003)/brokenurl#citeseer.ist.psu.edu/dhillon03informationtheoretic.htmlclustering, co-clustering, dhillon, information, theory, toreadConferenceProceedingsinproceedingsschmitz2006contentContent Aggregation on Knowledge Bases using Graph Clustering2006SchmitzChristophHothoAndreasJäschkeRobertStummeGerdSureYorkDomingueJohn530-5444011SpringerHeidelbergThe Semantic Web: Research and ApplicationsThe Semantic Web: Research and Applicationshttp://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdfLNAIRecently, 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.2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theoryReportunpublishedranade:sus{Some uses of spectral methods}2000RanadeA.G.unpublishedclustering, graph, spectral, svd, theoryConferenceProceedingsinproceedingsschmitz2006contentContent Aggregation on Knowledge Bases using Graph Clustering2006SchmitzChristophHothoAndreasJäschkeRobertStummeGerdSureYorkDomingueJohn530-5444011SpringerHeidelbergThe Semantic Web: Research and ApplicationsThe Semantic Web: Research and Applicationshttp://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdfLNAIRecently, 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.2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory