Brandes,Ulrik
Delling,Daniel
Gaertler,Marco
Görke,Robert
Hoefer,Martin
Nikoloski,Zoran
Wagner,Dorothea
On Finding Graph Clusterings with Maximum Modularity
Springer
4769
121-132
2007
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.
Schmitz,Christoph
Hotho,Andreas
Jäschke,Robert
Stumme,Gerd
Content Aggregation on Knowledge Bases using Graph Clustering
Springer
4011
530-544
2006
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.
Schmitz,Christoph
Hotho,Andreas
J\"aschke,Robert
Stumme,Gerd
Content Aggregation on Knowledge Bases using Graph Clustering
Springer
4011
530-544
2006
Schmitz,Christoph
Hotho,Andreas
Jäschke,Robert
Stumme,Gerd
Content Aggregation on Knowledge Bases using Graph Clustering
Springer
4011
530-544
2006
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.
Dhillon,I.S.
Mallela,S.
Modha,D.S.
Information-Theoretic Co-Clustering
89–98
2003
Newman,M. E.J.
The structure and function of complex networks
2003
Inspired 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.
Ng,AndrewY.
Jordan,MichaelI.
Weiss,Yair
On spectral clustering: Analysis and an algorithm
MIT Press
849–856
2001
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
Ranade,A.G.
Some uses of spectral methods
2000
Spielman,DanielA.
Teng,Shang
Spectral Partitioning Works: Planar Graphs and Finite Element Meshes
University of California at Berkeley
1996
Pothen,A.
Simon,H.D.
Liou,K.P.
Partitioning Sparse Matrices with Eigenvectors of Graphs
SIAM J. MATRIX ANAL. APPLIC.
11
430–452
1990
Donath,W.E.
Hoffman,A.J.
Lower bounds for the partitioning of graphs
IBM Journal of Research and Development
17
420–425
1973