The document proposes a discrete krill herd optimization algorithm for community detection in complex social networks. It introduces the motivation and challenges of community detection. The proposed approach adapts the krill herd algorithm domain to represent community structures using modularity as the objective function. Experimental results on four benchmark networks show the algorithm achieves good accuracy and high modularity, particularly for small to medium networks. Future work aims to improve performance on large networks through hybrid approaches.

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.