Graph mining with kernel self-organizing map

Motivations
Dissimilarities and distances between vertices
Kernel SOM
Application and comments
Graph mining with kernel self-organizing map
Nathalie Villa-Vialaneix
http://www.nathalievilla.org
Joint work with Fabrice Rossi, INRIA, Rocquencourt, France
Institut de Mathématiques de Toulouse, - IUT de Carcassonne, Université de
Perpignan
France
SanTouVal, February 1st, 2008
Nathalie Villa - nathalie.villa@math.univ-toulouse.fr SanTouVal - Feb. 2008

Motivations
Kernel SOM
Table of contents
1 Motivations
2 Dissimilarities and distances between vertices
3 Kernel SOM
4 Application and comments

Motivations
Kernel SOM
Exploring a big historic database
Data
1000 agrarian contracts,
from four seignories (about 10 villages) of South West of
France,
established between 1250 and 1350 (before the Hundred
Years’ war).

Motivations
Kernel SOM
Data
France,
Years’ war).
Historian’s questions:
family or geographical social links ?
central people having a main social role ?
. . .

Motivations
Kernel SOM
Data
France,
Years’ war).
Historian’s questions:
family or geographical social links ?
central people having a main social role ?
. . .
⇒ Data mining is required.

Motivations
Kernel SOM
A graph clustering problem
From the database, building a weighted graph:
with 615 vertices x1, . . . , xn := peasants found in the
contracts;

Motivations
Kernel SOM
contracts;
with weights (wi,j)i,j=1,...,n := {contracts where xi and xj are
mentionned}.

Motivations
Kernel SOM
contracts;
mentionned}.
Number of vertices: 615
Number of edges: 4193
Total of weights: 40 329
Diameter: 10
Density: 2,2%

Motivations
Kernel SOM
contracts;
mentionned}.
Number of vertices: 615
Number of edges: 4193
Total of weights: 40 329
Diameter: 10
Density: 2,2%
Clustering the vertices into homogeneous social groups to
understand the structure of the peasant community.

Motivations
Kernel SOM
Other ﬁelds modelized by large graphs
Computer science: World Wide Web, P2P network. . .
Social networks
Biology: Protein interactions, Neuronal network,. . .
Business, management: Transportation networks, Industry
partnerships. . .

Motivations
Kernel SOM
Social networks
partnerships. . .
Question: Understanding the structure of these large graphs
Clustering: building relevant homogeneous groups;
Graph drawing: giving a global representation of the graph.

Motivations
Kernel SOM
Social networks
partnerships. . .
Question: Understanding the structure of these large graphs
Clustering: building relevant homogeneous groups;
Graph drawing: giving a global representation of the graph.
Here: Self-Organizing Map for nonvectorial data.

Motivations
Kernel SOM
Usual dissimilarities between vertices
The Dice (Jaccard) index:
D(xi, xj) =
Γ(xi) ∩ Γ(xj)
|Γ(xi)| + |Γ(xj)|
(non weighted graphs);

Motivations
Kernel SOM
D(xi, xj) =
Γ(xi) ∩ Γ(xj)
|Γ(xi)| + |Γ(xj)|
Dissimilarities based on the shortest paths;

Motivations
Kernel SOM
D(xi, xj) =
Γ(xi) ∩ Γ(xj)
|Γ(xi)| + |Γ(xj)|
Dissimilarities based on the shortest paths;
Dissimilarities or distances based on the Laplacian matrix:
spectral clustering.

Motivations
Kernel SOM
Laplacian
Deﬁnitions
For a graph with vertices V = {x1, . . . , xn} having positive weights
(wi,j)i,j=1,...,n such that, for all i, j = 1, . . . , n, wi,j = wj,i and di = n
j=1 wi,j,
Laplacian: L = (Li,j)i,j=1,...,n where
Li,j =
−wi,j if i j
di if i = j
;

Motivations
Kernel SOM
Laplacian: property I [von Luxburg, 2007]
Connected subgraphs
KerL = Span{IA1
, . . . , IAk
} where Ai indicates the positions of the
vertices of the ith connected component of the graph.
1
4
5
2
3
KerL = Span





1
0
0
1
1


;


0
1
1
0
0






Motivations
Kernel SOM
Laplacian: property II [Boulet et al., 2008]
Perfect community : Complete subgraph (clique) which vertices
share the same neighbors outside the clique.
Laplacian and perfect communities
For a non weighted graph,
The graph has a perfect community with m vertices
⇔
L has m eigenvectors such that each eigenvector has the same
n − m coordinates that vanish.

Motivations
Kernel SOM
Application :

Motivations
Kernel SOM
Application :
But: only 1/3 of the graph can be drawn this way.

Motivations
Kernel SOM
Laplacian: property III [von Luxburg, 2007]
Min Cut problem: Suppose that we have a connected graph.
Find a classiﬁcation of the vertices of the graph, A1, . . . , Ak such
that
1
2
k
i=1 j∈Ai,j Ai
wj,j
is minimum , is equivalent to minimize
H = arg min
h∈Rn×k
Tr hT
Lh subject to
hT
h = I
hi = 1/
√
|Ai|1Ai

Motivations
Kernel SOM
that
1
2
k
i=1 j∈Ai,j Ai
wj,j
is minimum , is equivalent to minimize
H = arg min
h∈Rn×k
Tr hT
Lh subject to
hT
h = I
hi = 1/
√
|Ai|1Ai
⇒ NP-complete problem.

Motivations
Kernel SOM
that
1
2
k
i=1 j∈Ai,j Ai
wj,j
is minimum can be approached by
H = arg min
h∈Rn×k
Tr hT
Lh subject to hT
h = I

Motivations
Kernel SOM
that
1
2
k
i=1 j∈Ai,j Ai
wj,j
is minimum can be approached by
H = arg min
h∈Rn×k
Tr hT
Lh subject to hT
h = I
Spectral clustering: Find the k smallest eigenvectors of L, H, and
make the classiﬁcation on the rows of H.

Motivations
Kernel SOM
A regularized version of L
Regularization : the diffusion matrix : pour β > 0,
Kβ = e−βL
= +∞
k=1
(−βL)k
k! .
⇒
kβ
: V × V → R
(xi, xj) → K
β
i,j
diffusion kernel (or heat kernel).

Motivations
Kernel SOM
Diffusion process on the graph
If Z0 = (1 1 1 . . . 1 1)T
is the “energy” of each vertex at time 0 and
if a small fraction of this energy is propagated among the edges
of the graph at each time step, then after t steps, the energy of the
vertices of the graph is:
Zt = (1 + L)t
Z0

Motivations
Kernel SOM
Diffusion process on the graph
If Z0 = (1 1 1 . . . 1 1)T
is the “energy” of each vertex at time 0 and
if a small fraction of this energy is propagated among the edges
of the graph at each time step, then after t steps, the energy of the
vertices of the graph is:
Zt = (1 + L)t
Z0
Limits: Time step ∆t by t → t/(∆t) and → ∆t; then
(∆t) → 0 (continuous process) gives
lim Zt = e tL
= K t

Motivations
Kernel SOM
Properties
1 Diffusion on the graph: kβ(xi, xj) quantity of energy
accumulated in xj after a given time if energy 1 is injected in xi
at time 0 and if diffusion is done continuously along the edges.
β intensity of diffusion;

Motivations
Kernel SOM
Properties
2 Regularization operator: for u ∈ Rn
∼ V, uT
Kβu is higher for
vectors u that vary a lot over “close” vertices of the graph.
β intensity of regularization (for small β, direct neighbors are
more important);

Motivations
Kernel SOM
Properties
2 Regularization operator: for u ∈ Rn
∼ V, uT
Kβu is higher for
vectors u that vary a lot over “close” vertices of the graph.
β intensity of regularization (for small β, direct neighbors are
more important);
3 Reproducing kernel property: kβ is symmetric and positive
⇒ ∃ Hilbert space (H, ., . ) and φ : V → H such that
kβ
(xi, xj) = φ(xi), φ(xj) .

Motivations
Kernel SOM
Kohonen map
Mapping the data onto a 2 dimensional map
Each neuron of the map, i = 1, . . . , M is associated to a
prototype, pi ∈ H ;
Neurons are related to each others by a neighborhood
relationship (“distance”: d) :
Classifying the vertices on the map
Each xi is associated to a neuron (cluster or class) of the map,
f(xi).

Motivations
Kernel SOM
Preserving the initial topology
Energy
The goal is to minimize the energy of the map:
E =
M
i=1
h(d(f(x), i)) x − pi
2
H dP(x)
where h is a decreasing function (ex: h(t) = αe−t/2σ2
).

Motivations
Kernel SOM
Preserving the initial topology
Energy
The goal is to minimize the energy of the map:
E =
M
i=1
h(d(f(x), i)) x − pi
2
H dP(x)
where h is a decreasing function (ex: h(t) = αe−t/2σ2
).
Energy is approached by its empirical version:
En
=
n
j=1
M
i=1
h(d(f(xj), i)) xj − pi
2
H .
and minimization is approached by SOM algorithm.

Motivations
Kernel SOM
Batch kernel SOM [Villa and Rossi, 2007]
Initialize randomly γ0
ji
∈ R (i, j = 1, . . . , n) and p0
j
= n
i=1 γ0
ji
φ(xi).
Then, for l = 1, . . . , n repeat

Motivations
Kernel SOM
ji
∈ R (i, j = 1, . . . , n) and p0
j
= n
i=1 γ0
ji
φ(xi).
Assignment step
for all xi,
fl
(xi) = arg min
j=1,...,M
φ(xi) −
n
i=1
γl
jiφ(xi)
H

Motivations
Kernel SOM
ji
∈ R (i, j = 1, . . . , n) and p0
j
= n
i=1 γ0
ji
φ(xi).
Assignment step
for all xi,
fl
(xi) = arg min
j=1,...,M
φ(xi) −
n
i=1
γl
jiφ(xi)
H
Representation step
γl
j = arg min
γ∈Rn
n
i=1
h(fl
(xi), j) φ(xi) −
n
l =1
γl φ(xl )
2
H

Motivations
Kernel SOM
ji
∈ R (i, j = 1, . . . , n) and p0
j
= n
i=1 γ0
ji
φ(xi).
Assignment step
for all xi,
f(xi) = arg min
j=1,...,M
n
u,u =1
γjuγju kβ
(xu, xu ) − 2
n
u=1
γjukβ
(xu, xi)
Representation step
γl
ji =
h(fl
(xi), j))
n
i =1 h(fl(xi , j))

Motivations
Kernel SOM
Results on a 7 × 7 rectangular map

Motivations
Kernel SOM
Expected developments
1 Hierarchical clustering;
2 Achieve a classiﬁcation based on density criterium (joint work
with S. Gadat);
3 Adapting the algorithm to very large graphs (thousands of
vertices).

Motivations
Kernel SOM
References
Boulet, R., Jouve, B., Rossi, F., and Villa, N. (2008).
Batch kernel SOM and related laplacian methods for social network
analysis.
Neurocomputing.
To appear.
Villa, N. and Rossi, F. (2007).
A comparison between dissimilarity SOM and kernel SOM for clustering the
vertices of a graph.
In Proceedings of the 6th Workshop on Self-Organizing Maps (WSOM 07),
Bieleﬁeld, Germany.
von Luxburg, U. (2007).
A tutorial on spectral clustering.
Technical Report TR-149, Max Planck Institut für biologische Kybernetik.
Avaliable at http://www.kyb.mpg.de/publications/
attachments/luxburg06_TR_v2_4139%5B1%5D.pdf.

Graph mining with kernel self-organizing map

Recommandé

Recommandé

Contenu connexe

Similaire à Graph mining with kernel self-organizing map

Similaire à Graph mining with kernel self-organizing map (20)

Plus de tuxette

Plus de tuxette (20)

Dernier

Dernier (20)

Graph mining with kernel self-organizing map