cut: community update and tracking in dynamic social networks

Hao-Shang Ma and Jen-Wei Huang

Knowledge and Information Discovery Lab,Dept. of Electrical Engineering,

National Cheng Kung University

The 7th Workshop on Social Network Mining and Analysis (SNA-KDD'13) joint with the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'13)

CUT: Community Update and Tracking in Dynamic Social Networks

About Me Jen-Wei Huang (黃仁暐 )

Knowledge and Information Discovery Lab

Dept. of Electrical Engineering,

National Cheng Kung University

Email: jwhuang @ mail.ncku.edu.tw

http://kid.ee.ncku.edu.tw2013/11/22

KID Lab, National Cheng Kung University 2

ResearchData Mining and Database

◦Time Series Mining◦Social Network Analysis

Multimedia Information RetrievalUbiquitous Computing

◦Mobile Computing◦Cloud Computing

Bioinformatics

2013/11/22KID Lab, National Cheng Kung University 3

KID Lab, National Cheng Kung University

4

OutlineIntroductionCUT AlgorithmExperimentsConclusionsReferences

2013/11/22

IntroductionSocial networking websites allow

users to establish their own personal communities or social networks based on relationships of friends.

2012/10/12KID Lab, National Cheng Kung University 5

http://www.facebook.com/ http://twitter.com/


6

IntroductionBased on the relationships

between users, social networks exhibit a community structure.

2013/11/22


7

IntroductionThe detection of communities in a

network usually puts network nodes into groups in such a way that nodes in the same group are densely connected to one another.

An objective function is chosen to determine the quality of a community.

Modularity [1] is a measure of the quality of a partition in terms of the number of intra-community and inter-community edges.

2013/11/22


8

IntroductionSocial networks are always changing

with the time. We want to quickly and efficiently

identify the community structures of a network at every timestamp.

Updating the network structure by tracking previously known information instead of recalculating the relationships of all nodes and edges in the networks.

2013/11/22


9

IntroductionIn this work, we define the seed

of community, which is a collection of 3-cliques where any two of 3-cliques share more than one edge.

By tracking seed of communities, we are able to efficiently update and track the dynamics of communities in a social network.

2013/11/22


10

Example Network and 3-clique

2013/11/22


11

CUT AlgorithmWe propose CUT algorithm,

standing for Community Update and Tracking algorithm, to update and track seed of communities.

There are two phases in CUT algorithm.◦Initial phase, executed only once.

Find seed of communities Extend seed of communities to

communities◦Update and Tracking phase

Maintain and update CAB graph2013/11/22


12

Find Seed of Communities1. Find all 3-cliques in a network 2. Build CBA (Clique Bipartite

Adjacent) graph 3. Determine the seed of

communities in a network

2013/11/22


13

Find All 3-cliquesBacktracking algorithm

2013/11/22


14

All 3-cliques in the Network

2013/11/22


15

Clique Adjacent Bipartite Graph

2013/11/22


16

All 3-cliques in CAB

2013/11/22


17

Determine Seed of Community DFS-like algorithm to find connected

component

2013/11/22


18

CAB GraphThe complexity of tracking CAB is

lower than that of tracking the original graph ◦Complexity of building CAB is O(3|

C|)=O(|C|) ◦Complexity of determining the

connected component is O(3|C|)=O(|C|)

Easy to combine or split the seeds of community

2013/11/22


19

Extend to CommunitiesIgnore the sparse nodes whose degree

is smaller than 2. Assign the remain nodes to the closest

seed of communityClosest: the seed of community which has

the most links to the node

2013/11/22


20

Update and Tracking PhaseMaintain and Update CAB Graph

◦If there are some changes in the network, do the following cases Case 1: New nodes & new edges are

added Case 2: Old nodes & edges are removed

Extend to Communities

2013/11/22


21

Case 1: Merge and JoinNew Node : 20,21New Edge : (2,8)(5,20), (9,20),(11,21)

New 3-cliques: (2,6,8) and (5,9,20)

2013/11/22


22

Case 1: Merge and Join=(), =() If any two edges link to different seeds of

communities, Si and Sj , we merge(Si, Sj )Else if any edge of Ck links to any Si then we

Join(Si, Ck )

Complexity is O(3*| new C |) = O(| new C |)2013/11/22


23

Case 2: Split and RemovalIf there are nodes removed , we find

all edges which connect to the removed nodes

2013/11/22

N10 is removed.

Therefore,(4,10),(6,10)(8,10),(10,12)(10,11) are removed.


24

Node Removed Case - SplitRemove corresponding edges and cliquesRun FindSeedofCommunity algorithm again

to update to new seeds of communities

Complexity is O(3|C|+| removed C |)

2013/11/22


25

Joint CaseThere are new nodes added and edges removed at the same time

2013/11/22


26

Joint CaseWe simply deal with the Case 1 first,

and then deal with the Case 2 so that we can decrease the unnecessary splits.

Finally, extend seed of communities to communities.

2013/11/22


27

Related Works - Update the Community StructureNam P. Nguyen et al. propose a QCA

algorithm. [9]◦ The QCA algorithm uses the already known

community structure, and deal with the changing cases, new nodes, new edges, nodes removed, and edges removed based on modularity.

◦ In QCA algorithm, they keep the whole community structure at each timestamp.

◦ Using original CPM in removed case every time, which cost lots of time.

◦ They have to identifying the nodes or edges belong to which type of cases. It costs much time as well.

2013/11/22


28

ExperimentsCoauthor network

(2002~2010)◦ 1. About 20000

authors in one network

◦ 2. Densely connected graph

◦ 3. Five years as a time period, t1 is 2002-2006 (first update)

◦ 4. Variations of network at each time stamp are small

2013/11/22


29

Experiments

2013/11/22


30

Experimentsp2p-Gnutella

network◦ 1. t1-t4 is a

snapshot from August 4 to 7 2002, about 6000 nodes

◦ 2. Sparse connected graph

◦ 3. Variations of network at each time stamp are large.

2013/11/22


31

Experiments

2013/11/22


32

ConclusionsWe design CUT algorithm for updating

community structures in dynamic social networks instead of recalculating relationships of all nodes and edges in the social network.

Keeping seeds of communities in the memory at each timestamp is more efficient than keeping all communities.

Using Clique Adjacent Bipartite graph to update and track seeds of community leads to lower complexity.

2013/11/22


33

References1. M. E. J. Newman and M. Girvan, “Finding and

evaluating community structure in networks,” Phy. Rev. E 69, 2004.

2. Bowen Yan and Steve Gregory,” Detecting Communities in Networks by Merging Cliques,” ICIS, 2009.

3. CLAUSET, G., NEWMAN, M. E. and MOORE, C., “Finding community structure in very large networks,” Phys. Rev. E 70, 066111, 2004.

4. Zhengzhang Chen, Kevin A. Wilson, Ye Jin, William Hendrix and Nagiza F. Samatova, “Detecting and Tracking Community Dynamics in Evolutionary Networks,” ICDMW, 2010.

2013/11/22


34

References5. Yi Wang, Bin Wu, and Xin Pei, “CommTracker: A Core-

Based Algorithm of Tracking Community Evolution,” ADMA, 2008.

6. Nam P. Nguyen, Thang N. Dinh, Ying Xuan, and My T. Thai. “Adaptive Algorithms for Detecting Community Structure in Dynamic Social Networks,” INFOCOM, 2011.

7. Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte and Etienne Lefebvre,”Fast unfolding of communities in large networks,” JSTAT, 2008.

8. Nan Du, Bin Wu, Xin Pei, Bai Wang and Liutong Xu,” Community Detection in Large-Scale Social Networks,” SNA-KDD, 2007.

2013/11/22

cut: community update and tracking in dynamic social networks

Documents

tw20131122kid lab

seed of community

network nodes

tracking seed of communities

social network mining

network structure

community update

track seed of communities