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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-1
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Disassortative Degree Mixing and Information Diffusion for Overlapping Community Detection in Social Networks (DMID)
Mohsen Shahriari, Sebastian Krott, Ralf Klamma{shahriari, krott, klamma}@dbis.rwth-aachen.de
18.05.2015
Chair of Computer Science 5RWTH Aachen University
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-2
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Agenda
• Overlapping Community Detection (OCD)• Motivation• DMID• DMID for Time Evolving Networks
• Results• Zachary Karate Club• Evaluation measures and compared algorithms• Synthetic and real-world networks
• Conclusions & Future Works
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-3
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Overlapping Community Detection (OCD)
Detecting overlapping community structures in networks
Identifying overlapping nodes Two categories of algorithms
- Global approaches [Newman, Mark E. J. and Girvan 2004]
- Local approaches Leader-based methods [Chen et al. 2009; Stanoev et al. 2011]
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-4
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Motivation
Is the problem solved for all types of networks?- Real world networks have disassortative degree mixing
property Suggesting an algorithm working based on this property
- Competitive with other algorithms Running time Detecting hierarchical structure of graphs Identifying most influential nodes (leaders) Simple logic
- Identifying boundary spanners in learning environments Learning layers project
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-5
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID: A Two Phase Approach
First phase- Identifying most influential nodes- Using of disassortative degree mixing and degree- Identifying local leaders
Second phase- Cascading behavior- Network coordination game
Disassortative network
Assortative network
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-6
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID: Identifying Leaders
Detecting most influential nodes (leaders)- Using of disassortative degree mixing property
- Row normalize disassortative matrix
- Performing a random walk
- Computing local leadership value Combining degree and disassortative value
Cascading behavior named network coordination game
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-7
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID: Identifying leaders
Finding local leaders
Finding leaders using average follower degree (AFD)
ZacharyAFD=8
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-8
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID: Cascading behavior
Network coordination game- Cascades initiated by the identified leaders- Different cascades can overlap- Different cascade size
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-9
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-10
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-11
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-12
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
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00.33 1
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Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-13
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
1
01
1 2
2
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-14
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID:Cascading Behavior
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-15
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID for Time Evolving Networks
Using an optimization function for the first phase to detect the leaders - Adding/removing nodes/edges changes the leaders. How to
formulize? Some leaders might be removed (death or merge of communities) Some leaders might be added (split or birth of communities) Leaders do not change (growth or atrophy of communities, continuation) Optimization function?
Detecting cascade changes- How membership of nodes to communities change in each of the
above cases- How cascade sizes change?- How to formulize?
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-16
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Result on Zachary Karate Club A karate club with 34 nodes and 78 edges Node 1 and 34 as leaders 9, 31, 14, 3, 2 and 20 are overlapping nodes
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-17
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Evaluation Metrics and Compared Algorithms
Evaluation measures- NMI measure for networks with ground truth communities like LFR
networks [Lancichinetti et al. 2009]
- Extended modularity for evaluation of real-world networks [Nicosia et al. 2009]
Networks for testing and experiments- LFR synthetic networks [Lancichinetti and Fortunato 2009]
- Real world networks Implemented algorithms for comparison
- SSK [Stanoev et al. 2011]
- Clizz [Li et al. 2012]
- MONC [Havemann et al. 2011]
- Link Communities (LC) [Xie et al. 2013]
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-18
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
NMI Measure for LFR Networks
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-19
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
DMID Time Complexity vs Other Algorithms
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-20
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Results on Real-world Networks
Real-world datasets - Zachary karate club, Dolphin Networks, dblp, Email,
Facebook, Internet, Jazz, Hamsterster, Powergrid, Sawmill, Sawmill Strike
- DMID Highest modularity on Zachary, Sawmill Strike and Internet Second modularity rank on Jazz Best running on time on Email Second running time on dblp, Facebook, Internet and Powergrid DMID is competitive with selected algorithms
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-21
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Acknowledgement
Funding- Learning Layers Project
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-22
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
Conclusions and Future Works
A two-phase OCD algorithm is proposed- Disassortative degree mixing and information diffusion- Identifies local leaders and hierarchy of the network- Fuzzy membership of nodes to communities- Detecting leaders in social networks- Local nature
Can be implemented distributed
Improving the running time- Implementation with Pregel- Running on huge networks
Experiments on networks with different disassortativity degrees Extending DMID to the case of time evolving networks. How?
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. Jarke
Mohsen Shahriari
Sebastian KrottRalf Klamma
I5-MS-Monat10-23
Disassortative Degree Mixing
and Information Diffusion for Overlapping Community Detection in
Social Networks
(DMID)
Learning Layers
REFERENCES Duanbing Chen, Yan Fu, and Mingsheng Shang. 2009b. An efficient algorithm for overlapping community detection
in complex networks. Proceedings Of The 2009 WRI Global Congress On Intelligent Systems (2009), 244–247. DOI:10.1109/GCIS.2009.68
F. Havemann, M. Heinz, A. Struck, and J. Gläser. 2011. Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels. J. Stat. Mech. (2011). DOI:10.1088/1742-5468/2011/01/P01023
Andrea Lancichinetti and Santo Fortunato. 2009. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80, 1 (2009). DOI:10.1103/PhysRevE.80.016118
Andrea Lancichinetti, Santo Fortunato, and János Kertész. 2009. Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11, 3 (2009), 33015. DOI:10.1088/1367-2630/11/3/033015
H. J. Li, J. Zhang, Z. P. Liu, L. Chen, and X. S. Zhang. 2012. Identifying overlapping communities in social networks using multi-scale local information expansion. Eur Phys J B 85, 6 (2012). DOI:10.1140/epjb/e2012-30015-5
NEWMAN, MARK E. J. AND Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004).
V. Nicosia, G. Mangioni, V. Carchiolo, and M. Malgeri. 2009. Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech. 2009, 03 (2009), P03024. DOI:10.1088/1742-5468/2009/03/P03024
Angel Stanoev, Daniel Smilkov, and Ljupco Kocarev. 2011. Identifying communities by influence dynamics in social networks. Physical Review E 84, 4 (2011). DOI:10.1103/PhysRevE.84.046102