1 on the eigenvalue power law milena mihail georgia tech christos papadimitriou u.c. berkeley &
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On the Eigenvalue Power Law
Milena Mihail Georgia Tech
Christos PapadimitriouU.C. Berkeley
&
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Network and application studies need properties and models of:
Internet graphs & Internet Traffic.
Shift of networking paradigm: Open, decentralized, dynamic.
Intense measurement efforts. Intense modeling efforts.
Internet Measurement and Models
Routers
WWW
P2P
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Internet & WWW Graphs
http://www.etc
http://www.XXX.net
http://www.YYY.com
http://www.etc http://www.ZZZ.edu
http://www.XXX.com
http://www.etc
Routers exchanging traffic. Web pages and hyperlinks.
10K – 300K nodesAvrg degree ~ 3
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Real Internet Graphs
CAIDA http://www.caida.org
Average Degree = Constant
A Few Degrees VERY LARGE
Degrees not sharply concentrated around their mean.
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Degree-Frequency Power Law
degree1 3 4 5 102 100
freq
uen
cy
WWW measurement: Kumar et al 99Internet measurement: Faloutsos et
al 99
E[d] = const., but
No sharp concentration
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Degree-Frequency Power Law
1 3 4 5 102 100
freq
uen
cy
E[d] = const., but
No sharp concentration
degree
E[d] = const., but
No sharp concentration
Erdos-Renyi sharp concentration
Models by Kumar et al 00, x Bollobas et al 01, x Fabrikant et al 02
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Rank-Degree Power Law
rank
deg
ree
1 2 3 4 5 10
Internet measurement: Faloutsos et al 99
UUNET
SprintC&WUSA
AT&TBBN
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Eigenvalue Power Law
rank
eig
en
valu
e
1 2 3 4 5 10
Internet measurement: Faloutsos et al 99
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This Paper: Large Degrees & Eigenvalues
rank
eig
en
valu
es
1 2 3 4 5 10
UUNET
SprintC&WUSA
AT&TBBN2
34
2 3 4
deg
ree
s
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This Paper: Large Degrees & Eigenvalues
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Principal Eigenvector of a Star
11
1
11
1
1
1
d
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Large Degrees
2
3
4
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Large Eigenvalues
2
3
4
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Main Result of the Paper
The largest eigenvalues of the adjacency martix of a graph whose large degrees are power law distributed (Zipf), are also power law distributed.
Explains Internet measurements.
Negative implications for the spectral filtering method in information retrieval.
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Random Graph Model
let
Connectivity analyzed by Chung & Lu ‘01
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Random Graph Model
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Random Graph Model
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Theorem :
Ffor large enough
Wwith probability at least
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Proof : Step 1. Decomposition
Vertex Disjoint StarsLR-extra
RR
LL
LR =
-
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Proof: Step 2: Vertex Disjoint Stars
Degrees of each Vertex Disjoint Stars Sharply Concentrated around its Mean d_iHence Principal Eigenvalue Sharply Concentrated around
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Proof: Step 3: LL, RR, LR-extra
LR-extra has max degree
LL has
edges
RR has max degree
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Proof: Step 3: LL, RR, LR-extra
LR-extra has max degree
RR has max degree
LL has
edges
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Proof: Step 4: Matrix Perturbation Theory
Vertex Disjoint Stars have principal eigenvalues
All other parts have max eigenvalue QED
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Implication for Info Retrieval
Spectral filtering, without preprocessing, reveals only the large degrees.
Term-Norm Distribution Problem :
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Implication for Info Retrieval
Term-Norm Distribution Problem : Spectral filtering, without preprocessing, reveals only the large degrees.
Local information.
No “latent semantics”.
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Implication for Information Retrieval
Application specific preprocessing (normalization of degrees) reveals clusters:
WWW: related to searching, Kleinberg 97
IR, collaborative filtering, …
Internet: related to congestion, Gkantsidis et al 02
Open : Formalize “preprocessing”.
Term-Norm Distribution Problem :