LinkSCAN*: Overlapping Community Detection Using the Link-Space Transformation Sungsu Lim † , Seungwoo Ryu ‡ , Sejeong Kwon § , Kyomin Jung ¶ , and Jae-Gil Lee † † Dept. of Knowledge Service Engineering, KAIST ‡ Samsung Advanced Institute of Technology § Graduate School of Cultural Technology, KAIST ¶ Dept. of Electrical and Computer Engineering, SN ICDE 2014
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LinkSCAN *: Overlapping Community Detection Using the Link-Space Transformation
ICDE 2014. LinkSCAN *: Overlapping Community Detection Using the Link-Space Transformation. Sungsu Lim † , Seungwoo Ryu ‡ , Sejeong Kwon § , Kyomin Jung ¶ , and Jae-Gil Lee † † Dept . of Knowledge Service Engineering, KAIST ‡ Samsung Advanced Institute of Technology - PowerPoint PPT Presentation
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LinkSCAN*: Overlapping Community Detection Using the Link-Space Trans-formation
Sungsu Lim †, Seungwoo Ryu ‡, Sejeong Kwon§,Kyomin Jung ¶, and Jae-Gil Lee †
† Dept. of Knowledge Service Engineering, KAIST ‡ Samsung Advanced Institute of Technology§ Graduate School of Cultural Technology, KAIST¶ Dept. of Electrical and Computer Engineering, SNU
link-space transformation that transforms a given graph into the link-space graph
We develop an algorithm that performs a non-overlapping clustering on the link-space graph, which enables us to discover overlapping clustering
OriginalGraph
Overlap-ping
Communi-ties
LinkCommuni-
tiesLink-Space
Graph
Link-Space Transformation
Non-overlap-ping Clustering
Membership Translation
April 1,2014 9
Overall ProcedureWe propose an overlapping clustering al-
gorithm using the link-space transforma-tion
OriginalGraph
Overlap-ping
Communi-ties
LinkCommuni-
tiesLink-Space
Graph
Link-Space Transformation
Non-overlap-ping Clustering
Membership Translation
April 1,2014 10
Link-Space Transformation Topological structure
Each link of an original graph maps to a node of the link-space graph
Two nodes of the links-space graph are adjacent if the cor-responding two links of the original graph are incident
Weights Weights of links of the link-space graph are calculated from
the similarity of corresponding links of the original graph
65 7
k
8
4
i
1 2 3
j
0i1 j1
i0 i2
ik
j2 j3
j4jk
k5 k8
k6 k7𝑤 (𝑣𝑖𝑘 ,𝑣 𝑗𝑘 )=𝜎 (𝑒𝑖𝑘 ,𝑒 𝑗𝑘 )
April 1,2014 11
Overall ProcedureOverlapping clustering algorithm using the
link-space transformation
OriginalGraph
Overlap-ping
Communi-ties
LinkCommuni-
tiesLink-Space
Graph
Link-Space Transformation
Membership Translation
Non-overlap-ping Clustering
April 1,2014 12
Clustering on Link-Space Graph
Applying a non-overlapping clustering al-gorithm to the link-space graph
We use structural clustering that can as-sign a node into hubs or outliers (neutral membership)
Original graph Non-overlapping clustering on the link-space graph
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2
3
4
5
1/2
12
3413
23 35 45
003
1/2 1/2
1/211Another weights are less than 1/3
April 1,2014 13
Overall ProcedureOverlapping clustering algorithm using the
link-space transformation
OriginalGraph
Overlap-ping
Communi-ties
LinkCommuni-
tiesLink-Space
Graph
Link-Space Transformation
Membership Translation
Non-overlap-ping Clustering
April 1,2014 14
Membership TranslationMemberships of nodes of the link-space
graph map to the memberships of links of the original graph
Memberships of a node of the original graph are from the memberships of inci-dent links of the node
Membership translationNon-overlapping clustering on the link-space graph
1/2
12
3413
23 35 45
03
1/2 1/2
1/211
1
2
3
4
5
0
April 1,2014 15
Advantages of Link-Space Graph
Inheriting the advantages of the link-space graph, finding disjoint communities enables us to find overlapping communities where its original struc-ture is preserved since similarity properly reflect the structure of the original graph.
Easier to find overlapping communities
Preserving the orig-inal structure
Easier to find overlapping com-munities while preserving the original structure