Significant scales in community structure

Significant scales in community structure

V.A. Traag1,2, G. Krings3, P. Van Dooren4

1KITLV, Leiden, the Netherlands2e-Humanities, KNAW, Amsterdam, the Netherlands

3Real Impact, Brussels, Belgium,4UCL, Louvain-la-Neuve, Belgium

September 17, 2013

eRoyal Netherlands Academy of Arts and SciencesHumanities

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj) = −∑

c(ec − γn2c)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj) = −∑

c(ec − γn2c)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj) = −∑

c(ec − γn2c)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

Community Detection

Contant Potts Model (CPM)

• Minimize H(γ) = −∑ij(Aij − γ)δ(σi , σj) = −∑

c(ec − γn2c)

• Resolution-limit-free

• Internal density pc > γ

• Density between pcd < γ

How to choose γ?

Resolution profile

10−3 10−2 10−1 100103

104

105

106

γ

N E

Significance

How significant is a partition?

Significance

E = 14

E = 9

Fixed partition

E = 11

Better partition

Significance

E = 14

E = 9

Fixed partition

E = 11

Better partition

• Not: Probability to find E edges in partition.

• But: Probability to find partition with E edges.

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Subgraph probability

Decompose partition

• Probability to find partition with E edges.

• Probability to find communities with ec edges.

• Asymptotic estimate

• Probability for subgraph of nc nodes with density pc

Pr(S(nc , pc) ⊆ G (n, p)) ≈ exp[−n2cD(pc ‖ p)

]

Significance

• Probability for all communities Pr(σ) ≈∏c

exp[−n2cD(pc ‖ p)

].

• Significance S(σ) = − log Pr(σ) =∑c

n2cD(pc ‖ p).

Significance

10−3 10−2 10−1 100103

104

105

106

γ

N E

Significance

10−3 10−2 10−1 100103

104

105

106

γ

N E S

Benchmark

0.25

0.5

0.75

1

NM

In = 5000, Small

0

1S S∗

0 0.2 0.4 0.6 0.8 101

µ

S∗ 〈S〉

CPM+SigSignificanceModularity

InfomapOSLOM

Conclusions

• Scan γ efficiently.

• Significance applicable in all methods.

• Correct comparison to random graph.

Traag, Krings, Van Dooren Significant scales in Community StructurearXiv:1306.3398

Thank you!Questions?

e-mail: [email protected] twitter: @vtraag