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Overlapping community detection

Date post: 24-Feb-2016
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Overlapping community detection . Overlapping. Overlapping means that some vertices may belong to more than one community. agglomerativ E hierarchic A l clusterin G based on maxima L cliqu E. - PowerPoint PPT Presentation
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Overlapping community detection
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Page 1: Overlapping community detection

Overlapping community detection

Page 2: Overlapping community detection

Overlapping means that some vertices may belong to more than one community.

Overlapping

Page 3: Overlapping community detection

EAGLE algorithm is presented to uncover both the overlapping and hierarchical community structures of networks.

EAGLE algorithm has two stages:◦ 1. A dendrogram is generated.◦ 2. We choose an appropriate cut which breaks the

dendrogram into communities.

agglomerativE hierarchicAl clusterinG based on maximaL cliquE

Page 4: Overlapping community detection

1.Find out all maximal cliques in the network(Bron-Kerbosch algorithm).Note that not all maximal cliques are taken into account.We set a threshold k and neglecting all the maximal cliques with the size smaller than k.

The first stage

k=4 k=3

Page 5: Overlapping community detection
Page 6: Overlapping community detection

2.Select the pair of communities with the maximum similarity,incorporate them into a new one and calculate the similarity between the new community and other communities.

C1, C2 :community1,2 k:the degree of the vertexA:the adjacency matrix of the networkm:the total number of edges in the network

Page 7: Overlapping community detection

3.Repeat step 2 until only one community remains.

Stage 2: The task of the second stage of the

algorithm EAGLE is to cut the dendrogram.

Ov:the number of communities to which vertex v belongs.

Page 8: Overlapping community detection
Page 9: Overlapping community detection
Page 10: Overlapping community detection

Community Overlap PRopagation

Algorithm

Page 11: Overlapping community detection

Step1:every vertex is given a unique label. After few iterations the label of vertex is the set of pairs (c,b). c: community identifier b: belong coefficientEx.The label of vertex x={(1,0.2),(2,0.3),(3,0.5)}

Page 12: Overlapping community detection

Step2:each vertex x updates its label by replacing it by the label used by the greatest number of neighbours.

iteration tat the community for vertex oft coefficien belong the:),(

vertex of neighbors ofnumber total:)(identifiercommunity :

vertex theofneighbor the:

)(

),(),(

th

)(1

cxxcb

xxNc

xy

xN

ycbxcb

t

xNyt

t

Page 13: Overlapping community detection

Step3: if the algorithm satisfies the stop criterion , the algorithm stop.

itration at the identifiercommunity each with labelled verticesofnumber the:

}1:),{(

itration at the usein sidentifiercommunity ofset the:

)}0),((:{

0),(,

tht

xcbVxt

tht

tt

tc

iVcicc

ti

xcbVxVci

t

Page 14: Overlapping community detection

1-tt

t

11

mm assoon asn propagatio thestop wereduced.last sidentifier ofnumber thesince

identifiercommunity each with labelled verticesofnumber minimum the:otherwise

if)},min(),(),((:),{(

mcm

iiqpicqccpcqpicm

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ttttt

Page 15: Overlapping community detection

Ex.b

c

d

a

e

f

g

(b,1)

(d,1)

(c,1) (a,1)

(e,1)

(f,1)

(g,1)(initialize)

(first iteration)

(c,1/3)(d,1/3)(a,1/3)

(c,1/3)(b,1/3)(a,1/3)

(d,1/4)(b,1/4)(e,1/4)(g,1/4)

(f,1/3)(g,1/3)(a,1/3)

(e,1/3)(f,1/3)(a,1/3)

Threshold:1/vV:the maximum number of communities to which any vertex can belong.Ex.threshold=1/2

Page 16: Overlapping community detection

(second iteration)

(third iteration)

(c,1/3)(e,1/3)(b,1/6)(d,1/6)

(c,1/3)(e,1/3)(b,1/6)(d,1/6)

(c,2/4)(f,1/4)(e,1/4)

(e,5/6)(g,1/6)

(g,1/6)(f,1/3)(e,3/6)


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