networks community detection using artificial bee colony swarm optimization
DESCRIPTION
Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this work Artificial bee colony (ABC) optimization has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures when used as an objective function within ABC. Experiments on real life networks show the capability of the ABC to successfully find an optimized community structure based on the quality function used.TRANSCRIPT
NETWORKS COMMUNITY DETECTION USING ARTIFICIAL BEE COLONY SWARM
OPTIMIZATION
Ahmed Ibrahem Hafez, Hossam M. Zawbaa, Aboul Ella Hassanien, Aly A.
Fahmy and Vaclav Snasel
http://www.egyptscience.net
The 5th International Conference on Innovations in Bio-Inspired Computing and Applications, June 23-25, 2014
Agenda
1. Community Detection (CD) Problem.1. Community Detection (CD) Problem.
2. Proposed (ABC) algorithm.2. Proposed (ABC) algorithm.
3. Experimental Results.3. Experimental Results.
4. Conclusions.4. Conclusions.
Community Detection Problem• A Community: is a group of individuals
such that they interact with each other more frequently than with those outside the group.• a.k.a. group, cluster, module.
• Application • Network compression , visualization
of a huge network .• Can facilitate other SNA tasks.• Social studies : understanding the
interactions between people.
Community Detection Problem• Social network can be modeled as a
graph G = (V, E) consist of : • n node/actors.• m edges/interactions.
• Community detection is to divide the network into k communities.
• Community detection can be treated as an optimization problem:• given a quality measure of communities • Find a Community structure that max/min
1. Artificial Bee colony optimization (ABC)
2. Solution Representation
3. ABC Phases Details
4. Objectives (Quality measures)
3. Proposed (CD) algorithm.3. Proposed (CD) algorithm.
Artificial Bee colony optimization ABC• Artificial Bee colony optimization :
• A swarm based meta-heuristic that simulates foraging behavior of honey bees.• Contains three types of bees considering the division of labor
• Employee, onlooker, and scout bees.• The employer bees try to find food source and advertise them.• The onlooker bees follow their interesting employer.• The scout bee fly spontaneously to find/explore new food sources.
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
Solution Representation • Solution Representation : locus-based adjacency representation• Each food source consists of elements.
• A value j assigned to the i-th element means that nodes i and j are in the same community
• Decoding into communities takes linear time.• No prior knowledge about number of communities.• Example :
• is decoded into 3 groups
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
1
2
3 4
5
8 7
6Group 1
Group 2
Group 3
1 4 1 4 3 6 6 6
ABC Phases Details
• Initialization Phase : • All the vectors of the population of food sources are initialized by scout bees using the
following
• : is a random function that select randomly a node j form the node i’s neighbors.
• Employed Bees Phase :• Employed bees search for new food sources having more nectar
• Set
• where is a randomly selected food source, i is a randomly chosen parameter index.
• Greedy selection is apply to and
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
ABC Phases Details
• Onlooker Bees Phase : • select a food source by watching the dances of the employee bees and try to improve this source
• Employs a probabilistic approach to choose one of the food sources and follows its employed bee i.e. try to improve it using the same process as in the employed bee phase.
• The probability value of a food source is calculated as + 0.1
• Scout Bees Phase : • The scout bees employ a random flying pattern to discover new food source and replacing the
abandoned one with the new food source
• Using as in the initialization phase.
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
Objectives (Quality measures)
• Many community definitions has been proposed
• To be minimized• Conductance: measures the fraction of total edge volume that points outside the
community.• Expansion: measures the number of edges per node that point outside the community.• Internal Density: is the internal edge density of the community.• Cut Ratio : is the fraction of all possible edges leaving the community.• Normalized Cut: is the normalized fraction of edges leaving the community.• Maximum-ODF : (Out Degree Fraction) is the maximum fraction of edges of a node
pointing outside the community.• Average-ODF : is the average fraction nodes' edges pointing outside the community.• Flake-ODF: is the fraction of nodes that have fewer edges pointing inside than to the
outside of the community.
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
Objectives (Quality measures)
• Many community definitions has been proposed
• To be maximized• Modularity : measures the number of within-community edges, relative to a null
model of a random graph with the same degree distribution.• Community Score: measures the density of a sub-matrices based on volume and
row/column means.• Community Fitness: is the ratio between the total internal degrees of the nodes
belong to that community and the sum of the total internal and external degrees of the nodes belong to that community.
3. Proposed (ABC) algorithm.3. Proposed (ABC) algorithm.
Dataset used for the experiment4. Experimental Results.4. Experimental Results.
• Real Social Network • The Zachary Karate Club Network : It consists of 34 vertices and 78 edges. The network is
divided into two groups almost of the same size. • The Bottlenose Dolphin Network: It consists of 62 bottlenose dolphins. The network split
naturally into two large groups.• American College football Network : represents football games between American colleges
during a regular season in Fall 2000. The network is divided into 12 conferences.
• Performance Measures :• Normalized Mutual Information (NMI) is used to measure the similarity between the true
community structures and the detected ones.• Modularity : as a popular quality measure of community structures, we use it to calculate the
Modularity value of the detected community structures.
Summary of NMI values of the result for the real social network.
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Zachary Karate Bottlenose Dolphin American College football0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Avera
geO
DF
Avera
geO
DF
Avera
geO
DF
Conducta
nce
Conducta
nce
Conducta
nce
CutR
ati
o
CutR
ati
o
CutR
ati
o
Expansio
n
Expansio
n
Expansio
n
Fit
ness
Fit
ness
Fit
ness
Fla
keO
DF
Fla
keO
DF
Fla
keO
DF
Inte
rnalD
enis
ty
Inte
rnalD
enis
ty
Inte
rnalD
enis
ty
Max-O
DF
Max-O
DF Max-O
DF
Modula
rity
Modula
rity
Modula
rity
Norm
alizedC
ut
Norm
alizedC
ut
Norm
alizedC
ut
Score
Score
Score
AverageODF Conductance CutRatio Expansion Fitness FlakeODF InternalDenisty Max-ODF Modularity
NM
I
Summary of Modularity values of the result for the real social network.
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Zachary Karate Bottlenose Dolphin American College football0
0.1
0.2
0.3
0.4
0.5
0.6
Avera
geO
DF
Avera
geO
DF
Avera
geO
DF
Conduct
ance
Conduct
ance
Conduct
ance
CutR
ati
o
CutR
ati
o
CutR
ati
o
Expansi
on
Expansi
on
Expansi
on
Fitn
ess
Fitn
ess
Fitn
ess
FlakeO
DF
FlakeO
DF
FlakeO
DF
Inte
rnalD
enis
ty
Inte
rnalD
enis
ty
Inte
rnalD
enis
ty
Max-O
DF
Max-O
DF
Max-O
DF
Modula
rity
Modula
rity
Modula
rity
Norm
alize
dC
ut
Norm
alize
dC
ut
Norm
alize
dC
ut
Sco
re Sco
re
Sco
re
Ori
gin
al
Ori
gin
al
Ori
gin
al
AverageODF Conductance CutRatio Expansion Fitness FlakeODF InternalDenisty Max-ODF ModularityNormalizedCut Score Original
Modula
rity
Valu
e
Example : best result for The Zachary Karate Club
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Modularity Score - Fitness
Example : best result for The Zachary Karate Club
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Conductance Average-ODF
Example : best result for Bottlenose Dolphin Network
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Modularity Score
Example : best result for Bottlenose Dolphin Network
4. Experimental Results.4. Experimental Results.
Result for Real Social Network
Conductance Fitness
Case Study : Facebook dataset4. Experimental Results.4. Experimental Results.
• Online social network : a platform to build social networks and social relations among people.
• Share interests, activities, backgrounds, or real-life connections. • Online communities are formed where online users tend to form communities that
group users who share some common interest. • Facebook Dataset : is undirected social network which contain 3959 nodes and
84243 edges. • There is no clear community structure for the network • The network is studied in term of Modularity quality measure only.
Case Study : Facebook dataset4. Experimental Results.4. Experimental Results.
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Mo
du
lari
ty
Conclusions
• Artificial bee colony (ABC) is an optimization technique works effectively for the community detection problem.
• Performance is influenced directly by the objective quality function used in the optimization process.
• Experimental results show a promising result for the proposed algorithm. • Best result obtained using Modularity , Score and Fitness objectives• The algorithm detects community's number automatically.• Future work may focuses on setting some criteria for increasing the accuracy
and the scalability of algorithm.
Thank youThank you