%0 
%0 Journal Article
%A Newman, MEJ
%D 2006
%T Finding community structure in networks using the eigenvectors of matrices
%E 
%B Physical Review E
%C 
%I APS
%V 74
%6
%N 3
%P 36104
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F newman2006fcs
%K community, detection, graph, modularity, spectral, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A Chung, F. R. K.
%D 1997
%T Spectral Graph Theory
%E 
%B 
%C 
%I American Mathematical Society
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 book
%4 
%# 
%$ 
%F Chung:1997
%K graph, spectral, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Generic
%A Monien, B.
%D 2001
%T On Spectral Bounds for the k-Partitioning of Graphs
%E
%B 
%C
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 misc
%4 
%# 
%$ 
%F Monien_onspectral
%K graph, spectral, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Mohar, B.
%D 1991
%T The Laplacian spectrum of graphs
%E 
%B Graph Theory, Combinatorics, and Applications
%C 
%I New York: Wiley
%V 2
%6
%N 
%P 871--898
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F mohar1991lsg
%K graph, laplacian, spectral, survey, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Spielman, D.A.
%D 2007
%T Spectral Graph Theory and its Applications
%E 
%B Foundations of Computer Science, 2007. FOCS '07. 48th Annual IEEE Symposium on
%C 
%I 
%V 
%6
%N 
%P 29-38
%& 
%Y 
%S 
%7
%8 Oct.
%9
%? 
%! 
%Z 
%@ 0272-5428
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F 4389477
%K graph, spectral, theory
%X Spectral graph theory is the study of the eigenvalues and eigenvectors of matrices associated with graphs. In this tutorial, we will try to provide some intuition as to why these eigenvectors and eigenvalues have combinatorial significance, and will sitn'ey some of their applications.
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Johnson, David S. & Papadimitriou, Christos H.
%D 1988
%T On generating all maximal independent sets
%E 
%B Inf. Process. Lett.
%C 
%I Elsevier North-Holland, Inc.
%V 27
%6
%N 3
%P 119--123
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 0020-0190
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F 46243
%K complexity, graph, independent, sets, theory
%X 
%Z 
%U http://portal.acm.org/citation.cfm?id=46241.46243
%+
%^

%0 
%0 Journal Article
%A Dias, Vânia M.F.; de Figueiredo, Celina M.H. & Szwarcfiter, Jayme L.
%D 2005
%T Generating bicliques of a graph in lexicographic order
%E 
%B Theoretical Computer Science
%C 
%I 
%V 337
%6
%N 1-3
%P 240 - 248
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 0304-3975
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F Dias2005240
%K conp, graph, independent, set, theory
%X An independent set of a graph is a subset of pairwise non-adjacent vertices. A complete bipartite set B is a subset of vertices admitting a bipartition B=X[union or logical sum]Y, such that both X and Y are independent sets, and all vertices of X are adjacent to those of Y. If both X,Y[not equal to][empty set], then B is called proper. A biclique is a maximal proper complete bipartite set of a graph. We present an algorithm that generates all bicliques of a graph in lexicographic order, with polynomial-time delay between the output of two successive bicliques. We also show that there is no polynomial-time delay algorithm for generating all bicliques in reverse lexicographic order, unless P=NP. The methods are based on those by Johnson, Papadimitriou and Yannakakis, in the solution of these two problems for independent sets, instead of bicliques.
%Z 
%U http://www.sciencedirect.com/science/article/B6V1G-4FD0HTT-3/2/7efa1ee4d7b4823c7315a58b94f2f280
%+
%^

%0 
%0 Journal Article
%A Fiedler, M.
%D 1975
%T A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory
%E 
%B Czechoslovak Mathematical Journal
%C 
%I 
%V 25
%6
%N 100
%P 619--633
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F fiedler1975pen
%K graph, spectral, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Haveliwala, T.H. & Kamvar, S.D.
%D 2003
%T The second eigenvalue of the Google matrix
%E 
%B A Stanford University Technical Report http://dbpubs. stanford. edu
%C 
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F haveliwala8090seg
%K graph, pagerank, spectral, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Dhillon, Inderjit S.
%D 2001
%T Co-clustering documents and words using bipartite spectral graph partitioning
%E 
%B KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%I ACM Press
%V 
%6
%N 
%P 269--274
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 158113391X
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F coclustering01
%K community, detection, graph, spectral, theory
%X 
%Z 
%U http://portal.acm.org/citation.cfm?id=502512.502550
%+
%^

%0 
%0 Manuscript
%A Blelloch, Guy
%D 2002
%T Graph Separators
%E
%B 
%C
%I
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 unpublished
%4 
%# 
%$ 
%F graphseparators02
%K graph, separators, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Pothen, A.; Simon, H.D. & Liou, K.P.
%D 1990
%T Partitioning Sparse Matrices with Eigenvectors of Graphs
%E 
%B SIAM J. MATRIX ANAL. APPLIC.
%C 
%I 
%V 11
%6
%N 3
%P 430--452
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F partitioning89
%K clustering, community, graph, partitioning, spectral, theory
%X 
%Z 
%U http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf 
%+
%^

%0 
%0 Journal Article
%A Chan, Pak K.; Schlag, Martine D. F. & Zien, Jason Y.
%D 1994
%T Spectral K-way ratio-cut partitioning and clustering.
%E 
%B IEEE Trans. on CAD of Integrated Circuits and Systems
%C 
%I 
%V 13
%6
%N 9
%P 1088-1096
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F journals/tcad/ChanSZ94
%K community, detection, graph, partitioning, spectral, theory
%X 
%Z 
%U http://dblp.uni-trier.de/db/journals/tcad/tcad13.html#ChanSZ94
%+
%^

%0 
%0 Conference Proceedings
%A Yu, Stella X. & Shi, Jianbo
%D 2003
%T Multiclass Spectral Clustering
%E 
%B Proc. International Conference on Computer Vision (ICCV 03)
%C Nice, France
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 October
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F yu2003multiclass
%K Spectral, graph, partitioning, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Hagen, Lars W. & Kahng, Andrew B.
%D 1992
%T New spectral methods for ratio cut partitioning and clustering.
%E 
%B IEEE Trans. on CAD of Integrated Circuits and Systems
%C 
%I 
%V 11
%6
%N 9
%P 1074-1085
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F journals/tcad/HagenK92
%K graph, partitioning, spectral, theory
%X 
%Z 
%U http://dblp.uni-trier.de/db/journals/tcad/tcad11.html#HagenK92
%+
%^

%0 
%0 Conference Proceedings
%A Symeonidis, Panagiotis; Nanopoulos, Alexandros & Manolopoulos, Yannis
%D 2008
%T Tag recommendations based on tensor dimensionality reduction
%E 
%B RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 43--50
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 978-1-60558-093-7
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F 1454017
%K community, detection, graph, recommender, spectral, tag, theory
%X 
%Z 
%U http://portal.acm.org/citation.cfm?id=1454017
%+
%^

%0 
%0 Manuscript
%A Ranade, A.G.
%D 2000
%T Some uses of spectral methods
%E
%B 
%C
%I
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 unpublished
%4 
%# 
%$ 
%F ranade:sus
%K clustering, graph, spectral, svd, theory
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd
%D 2006
%T Content Aggregation on Knowledge Bases using Graph Clustering
%E Sure, York & Domingue, John
%B The Semantic Web: Research and Applications
%C Heidelberg
%I Springer
%V 4011
%6
%N 
%P 530-544
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F schmitz2006content
%K 2006, aggregation, clustering, content, graph, itegpub, l3s, myown, nepomuk, ontologies, ontology, seminar2006, theory
%X 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.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf
%+
%^

%0 
%0 Book Section
%A Brandes, Ulrik; Delling, Daniel; Gaertler, Marco; Görke, Robert; Hoefer, Martin; Nikoloski, Zoran & Wagner, Dorothea
%D 2007
%T On Finding Graph Clusterings with Maximum Modularity
%E Brandstädt, Andreas; Kratsch, Dieter & Müller, Haiko
%B Graph-Theoretic Concepts in Computer Science
%C Berlin / Heidelberg
%I Springer
%V 4769
%6
%N 
%P 121-132
%& 
%Y 
%S Lecture Notes in Computer Science
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 978-3-540-74838-0
%(
%)
%*
%L
%M
%1 
%2 Abstract - SpringerLink
%3 incollection
%4 
%# 
%$ 
%F springerlink:10.1007/978-3-540-74839-7_12
%K clustering, graph, modularity, theory
%X 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.
%Z 
%U http://dx.doi.org/10.1007/978-3-540-74839-7_12
%+
%^

%0 
%0 Book
%A Diestel, Reinhard
%D 2005
%T Graph Theory
%E 
%B 
%C 
%I Springer-Verlag Heidelberg, New York
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 3 (electronic edition)
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 book
%4 
%# 
%$ 
%F diestel2006graphentheorie
%K book, density, diesel, graph, theory
%X 
%Z 
%U http://www.math.ubc.ca/~solymosi/2007/443/GraphTheoryIII.pdf
%+
%^