1. 3.3 Network-Centric Community Detection
A Unified Process

2. 3.3 Network-Centric Community Detection
Comparison
Spectral clustering essentially tries to minimize the number of edges between groups.
Modularity consider the number of edges which is smaller than expected.
The spectral partitioning is forced to split the network into approximately equal-size clusters.

3. 3.4 Hierarchy-Centric Community Detection
Hierarchy-centric methods
build a hierarchical structure of communities based on network topology
two types of hierarchical clustering
Divisive
Agglomerative
Divisive Clustering
1. Put all objects in one cluster
2. Repeat until all clusters are singletons
a) choose a cluster to split
what criterion?
b) replace the chosen cluster with the sub-clusters
split into how many?

4. 3.4 Hierarchy-Centric Community Detection
Divisive Clustering
A Method: Cut the “weakest” tie
At each iteration, find out the weakest edge.
This kind of edge is most likely to be a tie connecting two communities.
Remove the edge.
Once a network is decomposed into two connected components, each component is considered a community.
Update the strength of links.
This iterative process is applied to each community to find sub-communities.

5. 3.4 Hierarchy-Centric Community Detection
Divisive Clustering
“Finding and evaluating community structure in networks,” M. Newman and M. Girvan, Physical Review, 2004
find the weak ties based on “edge betweenness”
Edge betweenness
the number of shortest paths between pair of nodes pass along the edge
utilized to find the “weakest” tie for hierarchical clustering
where
𝜎 𝑠𝑡 is the total number of shortest paths between nodes 𝑣 𝑠 and 𝑣 𝑡
𝜎 𝑠𝑡 ( 𝑒(𝑣 𝑖 , 𝑣 𝑗 )) is the number of shortest paths between nodes 𝑣 𝑠 and 𝑣 𝑡 that pass along the edge 𝑒(𝑣 𝑖 , 𝑣 𝑗 ).
𝐶 𝐵 𝑒( 𝑣 𝑖 , 𝑣 𝑗 ) = 𝑣 𝑠 , 𝑣 𝑡 ∈𝑉, 𝑠<𝑡 𝜎 𝑠𝑡 𝑒( 𝑣 𝑖 , 𝑣 𝑗 ) 𝜎 𝑠𝑡 𝑖𝑓 𝑖<𝑗 0 𝑖𝑓 𝑖=𝑗 𝐶 𝐵 𝑒( 𝑣 𝑗 , 𝑣 𝑖 ) 𝑖𝑓 𝑖>𝑗

6. 3.4 Hierarchy-Centric Community Detection
Divisive Clustering
The edge with higher betweenness tends to be the bridge between two communities
It is used to progressively remove the edges with the highest betweenness.

7. 3.4 Hierarchy-Centric Community Detection
Divisive Clustering
“Finding and evaluating community structure in networks,” M. Newman and M. Girvan, Physical Review, 2004
Example
Negatives for divisive clustering
edge betweenness-based scheme requires high computation
One removal of an edge will lead to the recomputation of betweenness for all edges

8. 3.4 Hierarchy-Centric Community Detection
Agglomerative Clustering
begins with base (singleton) communities
merges them into larger communities with certain criterion.
One example criterion: modularity
Let 𝑒 𝑖𝑗 be the fraction of edges in the network that connect nodes in community 𝑖 to those in community 𝑗
Let 𝑎 𝑖 = 𝑗 𝑒 𝑖𝑗 , then the modularity 𝑸= 𝒊 ( 𝒆 𝒊𝒊 − 𝒂 𝒊 𝟐 )
values approaching 𝑄=1 indicate networks with strong community structure
values for real networks typically fall in the range from 0.3 to 0.7
동일한 Community 안의 Edge 수 – 서로 다른 Community 들 간의 Edge 수

9. 3.4 Hierarchy-Centric Community Detection
Agglomerative Clustering
Two communities are merged if the merge results in the largest increase of overall modularity
The merge continues until no merge can be found to improve the modularity.
Dendrogram according to Agglomerative Clustering based on Modularity

10. 3.4 Hierarchy-Centric Community Detection
Agglomerative Clustering
In the dendrogram, the circles at the bottom represent the individual nodes of the network.
As we move up the tree, the nodes join together to form larger and larger communities, as indicated by the lines, until we reach the top, where all are joined together in a single community.
Alternatively, the dendrogram depicts an initially connected network splitting into smaller and smaller communities as we go from top to bottom.
A cross section of the tree at any level, such the one indicated by a dotted line, will give the communities at that level.

11. 3.4 Hierarchy-Centric Community Detection
Divisive vs. Agglomerative Clustering
Zachary's karate club study
Zachary observed 34 members of a karate club over a period of two years. During the course of the study, a disagreement developed between the administrator (34) of the club and the club's instructor (1), which ultimately resulted in the instructor's leaving and starting a new club, taking about a half of the original club's members with him

12. 3.4 Hierarchy-Centric Community Detection
Divisive vs. Agglomerative Clustering
Divisive
“Community structure in social and biological networks”, Michelle Girvan, and M. E. J. Newman, 2001  Using edge-betweeness
Agglomerative
“Fast algorithm for detecting community structure in networks”, M. E. J. Newman, 2003  Using modularity
Divisive
Agglomerative

13. Summary of Community Detection
Node-Centric Community Detection
cliques, k-cliques, k-clubs
Group-Centric Community Detection
quasi-cliques
Network-Centric Community Detection
Clustering based on vertex similarity
Latent space models, block models, spectral clustering, modularity maximization
Hierarchy-Centric Community Detection
Divisive clustering
Agglomerative clustering

14. 3.5 Community Evaluation
Here, we consider a “Social Network with Ground Truth”
Community membership for each actor is known  an ideal case
For example,
A synthetic networks generated based on predefined community structures
L. Tang and H. Liu. “Graph mining applications to social network analysis.” In C. Aggarwal and H.Wang, editors, Managing and MiningGraph Data, chapter 16, pages Springer, 2010b
Some well-studied tiny networks like Zachary’s karate club with 34 members
M.Newman. “Modularity and community structure in networks.” PNAS, 103(23): , 2006a.
Simple comparison between the ground truth with the identified community structure
Visualization
One-to-one mapping

15. 3.5 Community Evaluation
The number of communities after grouping can be different from the ground truth
No clear community correspondence between clustering result and the ground truth
Normalized Mutual Information (NMI) can be used
How to measure the
clustering quality?
Each number denotes a node, and each circle or block denotes a community 
1) Both communities {1, 3} and {2} map to the community {1, 2, 3} in the ground truth
2) The node 2 is wrongly assigned

16. 3.5 Community Evaluation Entropy 확률변수의 불확실성을 측정하기 위한 것
Measure of disorder
The information volume contained in a random variable X (or in a distribution X)
X의 엔트로피는 X의 모든 가능한 결과값 x에 대해 x의 발생 확률과 그 확률의 역수의 로그 값의 곱의 합
일반적으로 지수 b의 값으로서 2나 오일러의 수 e, 또는 10이 많이 사용된다. b=2인 경우에는 엔트로피의 단위가 비트(bit)이며, b=e이면 네트(nat), 그리고 b=10인 경우에는 디짓(digit)이 된다.
𝐻 𝑋 =− 𝑥∈𝑋 𝑝 𝑥 𝑙𝑜𝑔 𝑏 (𝑥)

17. 3.5 Community Evaluation Entropy와 동전 던지기 [from wikipedia]
앞면과 뒷면이 나올 확률이 같은 동전을 던졌을 경우의 엔트로피를 생각해 보자. 이는 H,T 두 가지의 경우만을 나타내므로 엔트로피는 1이다.
𝐻 𝑋 =− 𝑥∈𝑋 𝑝 𝑥 𝑙𝑜𝑔 𝑏 𝑥 = −( 1 2 × 𝑙𝑜𝑔 × 𝑙𝑜𝑔 )=1
한편 공정하지 않는 동전의 경우에는 특정 면이 나올 확률이 상대적으로 더 높기 때문에 엔트로피는 1보다 작아진다. 우리가 예측해서 맞출 수 있는 확률이 더 높아졌기 때문에 정보의 양, 즉 엔트로피는 더 작아진 것이다. 동전던지기의 경우에는 앞,뒤 면이 나올 확률이 1/2로 같은 동전이 엔트로피가 가장 크다.
엔트로피를 불확실성(uncertainity)과 같은 개념이라고 인식할 수 있다.
불확실성이 높아질수록 정보의 양은 더 많아지고 엔트로피는 더 커진다.

18. 3.5 Community Evaluation Mutual Information (상호 정보량)
It measures the shared information volume between two random variables (or two distributions)
두 확률 변수 (또는 두 분포) X, Y가 얼마나 밀접한 관계가 있는지 또는 얼마나 서로간에 의존을 하는지를 측정
국문 참고 문헌

19. 3.5 Community Evaluation
Normalized Mutual Information (NMI, 정규화된 상호 정보량)
It measures the shared information volume between two random variables (or two distributions)
두 확률 변수 (또는 두 분포) X, Y가 얼마나 밀접한 관계가 있는지를 측정
The values is between 0 and 1
Consider a partition as a random variable, we can compute the matching quality between ground truth and the identified clustering

20. 3.5 Community Evaluation NMI Example (1/2)
Partition a ( 𝜋 𝑎 ): [1, 1, 1, 2, 2, 2]
Partition b ( 𝜋 𝑏 ): [1, 2, 1, 3, 3, 3]
𝜋 𝑎
1, 2, 3
4, 5, 6
𝜋 𝑏
1, 3 2
4, 5,6

21. 3.5 Community Evaluation =0.8278 NMI Example (2/2)
Partition a ( 𝜋 𝑎 ): [1, 1, 1, 2, 2, 2]
Partition b ( 𝜋 𝑏 ): [1, 2, 1, 3, 3, 3]
𝜋 𝑎
1, 2, 3
4, 5, 6
𝜋 𝑏
1, 3 2
4, 5,6
=0.8278

22. 3.5 Community Evaluation Accuracy of Pairwise Community Memberships
Consider all the possible pairs of nodes and check whether they reside in the same community
An error occurs if
Two nodes belonging to the same community are assigned to different communities after clustering
Two nodes belonging to different communities are assigned to the same community
Construct a contingency table

23. 3.5 Community Evaluation Accuracy = (4+9)/ (4+2+9+0) = 0.86
Accuracy of Pairwise Community Memberships
Ground Truth
1, 2, 3
4, 5, 6
1, 3 2
Clustering Result
Accuracy = (4+9)/ ( ) = 0.86

24. 3.5 Community Evaluation Accuracy of Pairwise Community Memberships
Balanced Accuracy (BAC) = 1 – Balanced Error Rate (BER)
𝐵𝐴𝐶= 𝑎 𝑎+𝑐 + 𝑑 𝑏+𝑑 =1 −𝐵𝐸𝑅
𝐵𝐸𝑅= 1 2 ( 𝑐 𝑎+𝑐 + 𝑏 𝑏+𝑑 )
This measure assigns equal importance to “false positives” and “false negatives”, so that trivial or random predictions incur an error of 0.5 on average.

25. 3.5 Community Evaluation 𝐵𝐴𝐶= 1 2 4 6 + 9 9 = 0.83
Accuracy of Pairwise Community Memberships
Balanced Accuracy (BAC) = 1 – Balanced Error Rate (BER)
𝐵𝐴𝐶= 𝑎 𝑎+𝑐 + 𝑑 𝑏+𝑑 =1 −𝐵𝐸𝑅
𝐵𝐸𝑅= 1 2 ( 𝑐 𝑎+𝑐 + 𝑏 𝑏+𝑑 )
𝐵𝐴𝐶= = 0.83

26. 동일한 Community 안의 Edge 수 – 서로 다른 Community 들 간의 Edge 수
3.5 Community Evaluation
Evaluation without Ground Truth
This is the most common situation
Quantitative evaluation functions: modularity
Once we have a network partition, we can compute its modularity
The method with higher modularity wins
modularity
Let 𝑒 𝑖𝑗 be the fraction of edges in the network that connect nodes in community 𝑖 to those in community 𝑗
Let 𝑎 𝑖 = 𝑗 𝑒 𝑖𝑗 , then the modularity 𝑸= 𝒊 ( 𝒆 𝒊𝒊 − 𝒂 𝒊 𝟐 )
values approaching 𝑄=1 indicate networks with strong community structure
values for real networks typically fall in the range from 0.3 to 0.7
동일한 Community 안의 Edge 수 – 서로 다른 Community 들 간의 Edge 수

27. Book Available at Morgan & claypool Publishers Amazon
If you have any comments, please feel free to contact:
Lei Tang, Yahoo! Labs,
Huan Liu, ASU