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A Discrete Krill Herd Optimization Algorithm for
Community Detection
11th International Computer Engineering Conference , Cairo university 2015.
Khaled Ahmed
http://www.egyptscience.net
Overview
 Introduction
Motivation
Problem Definition
 Related Work
 Proposed Approach
 Results and Discussion
 Conclusion and Future Works
11th International Computer Engineering Conference , Cairo university 2015.
)
2
Introduction
3
11th International Computer Engineering Conference , Cairo university 2015.)
The rapid increase on the social networks present an urgent need for identifying
the community structure.
social network is a set of nodes which represent users or profiles and a set of
edges, which represents the interaction between nodes it could be internal or
external connectivity.
Motivation
4
11th International Computer Engineering Conference , Cairo university 2015.
Can we get Valuable insights for these social networks ?
One of these Valuable insights is ‘Community detection ‘ .
Why Community detection ?
- Its help in many analysis the social networks:
citation network might represent related papers on a single topic , represent
pages of related topics, easy to visualize and understand. ƒ
Problem Definition
 Finding Communities in complex network such as Online Social network is
a difficult task, many challenges .
 Scalability: such networks can be huge, often in a scale of millions of actors
and hundreds of millions of connections, Existing Community detection
techniques might fail when applied directly to networks of this size.
 Heterogeneity: In reality, multiple relationships can exist between
individuals, and multiple types of entities can also be involved in one
network.
 Evolution: Social network emphasizes timeliness, which makes the network
dynamic and changes over time.
 Evaluation: the task of comparison and evaluation different work in
community detection is also a challenging process, because of the lack of
11th International Computer Engineering Conference , Cairo university 2015.
5
Related work
 There are common algorithms used for
community detection :
 Discrete BAT Algorithm.
 Artificial fish swarm algorithm.
 Infomap . Fast Greedy. label propagation.
Multilevel . Walktrap.
11th International Computer Engineering Conference , Cairo university 2015.
6
Contribution of the paper
 Change the krill algorithm domain
 Algorithm redesign
 Present discrete krill algorithm
 Comparing our results with 7 popular algorithm
over four benchmarks
7
Proposed Approach
 Krills [21] move in herd searching for food and the food
makes the herd is divided into groups with minimum distance
between krill , food and calculate an objective function with
highest density krills groups
basic steps :
 Induced Motion (N).
 Foraging Motion (F).
 Random diffusion (D).
11th International Computer Engineering Conference , Cairo university 2015.
8
Proposed Approach
11th International Computer Engineering Conference , Cairo university 2015.
9
Results and discussion
 We used four social networks which are The Zachary
Karate , Club,The Bottlenose Dolphin ,American College
football anda Facebook data set.
 We compared our results againts Discrete BAT
Algorithm . Artificial fish swarm algorithm .
 Infomap . Fast Greedy . label propagation.
 Multilevel . Walktrap .
11th International Computer Engineering Conference , Cairo university 2015.
10
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
11
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
12
Results and discussion
SRGE workshop in Cairo University Conference Hall (12-
September-2015)
13
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
14
AKHSO’s community
detection for Facebook
data set.
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
15
AKHSOs community detection for
Bottlenose Dolphin data set.
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
16
AKHSOs community detection
for Zachary Karate data set.
Results and discussion
11th International Computer Engineering Conference , Cairo university 2015.
17
AKHSOs community detection for
college Football data set.
Conclusion
 discrete Krill herd algorithm for complex social network’s community detection. The
locus-based adjacency scheme is used for two reasons first for encoding and
decoding tasks , the second for representing a community structure. A discrete Krill
herd uses Modularity as an objective function in the optimization process.
 This research presents a quite promising accuracy and high Modularity results for
community structure. It presents good results for small and medium size of data sets,
although it presents medium results over big data sets .
11th International Computer Engineering Conference , Cairo university 2015.
18
Future Works
 Future Works of this research is to enhance
it’s
 performance over big data sets, presents
hybrid krill herd with other optimization
algorithm. Presents a comparative study for
more than Swarm optimization algorithm over
social networks based on experimental results.
11th International Computer Engineering Conference , Cairo university 2015.
19
Thanks and Acknowledgement
20
11th International Computer Engineering Conference , Cairo university 2015.

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A Discrete Krill Herd Optimization Algorithm for Community Detection

  • 1. A Discrete Krill Herd Optimization Algorithm for Community Detection 11th International Computer Engineering Conference , Cairo university 2015. Khaled Ahmed http://www.egyptscience.net
  • 2. Overview  Introduction Motivation Problem Definition  Related Work  Proposed Approach  Results and Discussion  Conclusion and Future Works 11th International Computer Engineering Conference , Cairo university 2015. ) 2
  • 3. Introduction 3 11th International Computer Engineering Conference , Cairo university 2015.) The rapid increase on the social networks present an urgent need for identifying the community structure. social network is a set of nodes which represent users or profiles and a set of edges, which represents the interaction between nodes it could be internal or external connectivity.
  • 4. Motivation 4 11th International Computer Engineering Conference , Cairo university 2015. Can we get Valuable insights for these social networks ? One of these Valuable insights is ‘Community detection ‘ . Why Community detection ? - Its help in many analysis the social networks: citation network might represent related papers on a single topic , represent pages of related topics, easy to visualize and understand. ƒ
  • 5. Problem Definition  Finding Communities in complex network such as Online Social network is a difficult task, many challenges .  Scalability: such networks can be huge, often in a scale of millions of actors and hundreds of millions of connections, Existing Community detection techniques might fail when applied directly to networks of this size.  Heterogeneity: In reality, multiple relationships can exist between individuals, and multiple types of entities can also be involved in one network.  Evolution: Social network emphasizes timeliness, which makes the network dynamic and changes over time.  Evaluation: the task of comparison and evaluation different work in community detection is also a challenging process, because of the lack of 11th International Computer Engineering Conference , Cairo university 2015. 5
  • 6. Related work  There are common algorithms used for community detection :  Discrete BAT Algorithm.  Artificial fish swarm algorithm.  Infomap . Fast Greedy. label propagation. Multilevel . Walktrap. 11th International Computer Engineering Conference , Cairo university 2015. 6
  • 7. Contribution of the paper  Change the krill algorithm domain  Algorithm redesign  Present discrete krill algorithm  Comparing our results with 7 popular algorithm over four benchmarks 7
  • 8. Proposed Approach  Krills [21] move in herd searching for food and the food makes the herd is divided into groups with minimum distance between krill , food and calculate an objective function with highest density krills groups basic steps :  Induced Motion (N).  Foraging Motion (F).  Random diffusion (D). 11th International Computer Engineering Conference , Cairo university 2015. 8
  • 9. Proposed Approach 11th International Computer Engineering Conference , Cairo university 2015. 9
  • 10. Results and discussion  We used four social networks which are The Zachary Karate , Club,The Bottlenose Dolphin ,American College football anda Facebook data set.  We compared our results againts Discrete BAT Algorithm . Artificial fish swarm algorithm .  Infomap . Fast Greedy . label propagation.  Multilevel . Walktrap . 11th International Computer Engineering Conference , Cairo university 2015. 10
  • 11. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 11
  • 12. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 12
  • 13. Results and discussion SRGE workshop in Cairo University Conference Hall (12- September-2015) 13
  • 14. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 14 AKHSO’s community detection for Facebook data set.
  • 15. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 15 AKHSOs community detection for Bottlenose Dolphin data set.
  • 16. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 16 AKHSOs community detection for Zachary Karate data set.
  • 17. Results and discussion 11th International Computer Engineering Conference , Cairo university 2015. 17 AKHSOs community detection for college Football data set.
  • 18. Conclusion  discrete Krill herd algorithm for complex social network’s community detection. The locus-based adjacency scheme is used for two reasons first for encoding and decoding tasks , the second for representing a community structure. A discrete Krill herd uses Modularity as an objective function in the optimization process.  This research presents a quite promising accuracy and high Modularity results for community structure. It presents good results for small and medium size of data sets, although it presents medium results over big data sets . 11th International Computer Engineering Conference , Cairo university 2015. 18
  • 19. Future Works  Future Works of this research is to enhance it’s  performance over big data sets, presents hybrid krill herd with other optimization algorithm. Presents a comparative study for more than Swarm optimization algorithm over social networks based on experimental results. 11th International Computer Engineering Conference , Cairo university 2015. 19
  • 20. Thanks and Acknowledgement 20 11th International Computer Engineering Conference , Cairo university 2015.