Multi-objective NSGA-II based community detection using dynamical evolution social network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 5, October 2021, pp. 4502~4512
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i5.pp4502-4512  4502
Journal homepage: http://ijece.iaescore.com
Multi-objective NSGA-II based community detection using
dynamical evolution social network
Muhammed E. Abd. Alkhalec Tharwat1
, Mohd Farhan Md. Fudzee2
, Shahreen Kasim3
, Azizul Azhar
Ramli4
, Mohammed K. Ali5
1,2,3,4
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja,
Malaysia
5
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia
Article Info ABSTRACT
Article history:
Received Sep 1, 2020
Revised Feb 23, 2021
Accepted Mar 22, 2021
Community detection is becoming a highly demanded topic in social
networking-based applications. It involves finding the maximum intra-
connected and minimum inter-connected sub-graphs in given social
networks. Many approaches have been developed for community’s detection
and less of them have focused on the dynamical aspect of the social network.
The decision of the community has to consider the pattern of changes in the
social network and to be smooth enough. This is to enable smooth operation
for other community detection dependent application. Unlike dynamical
community detection Algorithms, this article presents a non-dominated
aware searching Algorithm designated as non-dominated sorting based
community detection with dynamical awareness (NDS-CD-DA). The
Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with
two objectives: modularity and normalized mutual information (NMI).
Experimental results on synthetic networks and real-world social network
datasets have been compared with classical genetic with a single objective
and has been shown to provide superiority in terms of the domination as well
as the convergence. NDS-CD-DA has accomplished a domination percentage
of 100% over dynamic evolutionary community searching DECS for almost
all iterations.
Keywords:
Community detection
Complex network
Dynamic social networks
Modularity
Multi-objective
NSGA-II
This is an open access article under the CC BY-SA license.
Corresponding Author:
Muhammed E Abd Alkhalec Tharwat
Faculty of Computer Science and Information Technology
Universiti Tun Hussein Onn Malaysia
86400 Parit Raja Batu Pahat Johor, Malaysia
Email: gi170023@siswa.uthm.edu.my
1. INTRODUCTION
The community detection CD is one of the problems in the social network (SN) applications [1]-[3].
It aims at partitioning the social network into a set of disjoint communities with achieving maximum intra-
community similarity and minimum inter-community similarity [4]. However, most of the approaches
developed for CD in the literature deal with SN from a static perspective which makes the community
detection not suitable for handling the progressive increase in the social network. Hence, it is important to
develop a model that provides not only satisfying modularity which is related to the quality of the community
detection decision but also a satisfying smoothness which indicates to the smooth changes in the community
partitioning decision concerning time [5]. Meta-heuristic searching is a set of a random searching Algorithm
that contains some heuristics for the goal of optimizing a certain set of objectives [6], [7]. The meta-heuristic
searching Algorithm encodes the solution of the problem and creates a set of candidate solutions, then it

Int J Elec & Comp Eng ISSN: 2088-8708 
Multi-objective NSGA-II based community detection using… (Muhammed E Abd Alkhalec Tharwat)
4503
evaluates them based on the objective function. The good solutions are favoured and get more influence on
creating other solutions, while remaining solutions get less attention. Some of the famous approaches of
meta-heuristic searching algorithms are particle swarm optimization [8] and genetic algorithms [9]. Using
meta-heuristics approaches for the goal of community detection was proposed by many researchers [10]-[12].
However, most of the approaches have not tackled the problem from the perspective of multi-objective with
considering the evolving of the network as an aspect of performance. This goal opens the new direction of
development of community detection methods based on the concept of non-dominated sorting or multi-
objective meta-heuristic algorithms [13]. There are many multi-objective meta-heuristic searching
algorithms. Most of them use the concept of non-dominated sorting as the main criterion in ranking the
solutions while searching. One of the state-of-the-art approaches for multi-objective meta-heuristic searching
is the non-dominated sorting genetic algorithm NSGA-II [14], [15]. It uses two concepts for providing the
final set of non-dominated solutions. The first one is non-dominated sorting and the second one is the
crowding distance. The role of the non-dominated sorting is to enable exploitation and the role of the
crowding is to enable exploration. The balance between both is by giving priority to the rank of the solution
for the goal of exploitation or domination and then gives priority to the crowding distance for the goal of
exploration. The goal of the article is to present a non-dominated aware searching Algorithm designated as
non-dominated sorting based community detection with dynamical awareness NDS-CD-DA. The problem
will be formulated as a multi-objective optimization problem. The objectives will consider not only
community detection quality in terms of static aspect but it will include dynamic orientation for the sake of
accomplishing smooth transition over time from community detection decision and another. The remaining
of the article is organized is being as. The related work is presented in section 2. Next, the methodology is
provided in section 3. Afterwards, the evaluation and experimental work are given in section 4. Lastly, the
conclusion and future work are provided in section 5.
2. RELATED WORKS
Numerous articles in the literature have done the tackling of the community detection Algorithm
from the perspective of multi-objective optimization. Many researchers have assumed convexity of the
problem and solve it using the sum of objectives such as the work of [16] and the work of [17], while less of
them have separated the objectives and handled the problem using Pareto concept. Some researchers have
formulated the problem using both inter and intra distance and found a set of solutions that balances between
them with non-domination aspect [18]. This does not follow the classical non-dominated solutions searching
Algorithms of multi-objectives. However, many researchers have adopted traditional multi-objective
evolutionary searching optimization.
While some researchers have focused on the community structure and its influence on the resilience
of the network as a whole [19], many researchers have focused on the community detection from the
perspective of multi-objective optimization. In the work of [16], the multi-objective evolutionary Algorithm
has been used for identifying overlapping community structure in a complex network. This work has ignored
the dynamic aspect of the network and its temporal changes. In the work of [20] multi-objective genetic
optimization for minimizing the external connectivity as one objective and maximizing the internal
connectivity as another objective. The author Bello-Orgaz of [20] use for external connectivity, expansion,
separability and cut ratio and use for maximizing the internal connectivity the density, triangle participation
ratio, clique number and clustering coefficient. However, their optimization does not include any term for
achieving the smoothness that is required to handle the dynamics. The work of [21] has used temporal change
as an objective along with the modularity which expresses the quality of the detection, however, in their
multi-objective optimization there non-incorporation of non-dominated sorting [22].
In the work of [23], the similarity function of the unsigned network has been extended to include the
singed network. Their evolutionary multi-objective optimization network has a crossover merging operator
that enables cluster merging while searching. For multi-objective, they derived negatively correlated
objective functions based on the signed network to accomplish better performance. However, their work has
ignored the aspect of dynamic change optimization of the evolving social network. One of the famous
approaches of community detection named a label propagation-based algorithm (LPAm) was also developed
by using meta-heuristics.
In the work of [24], ant colony optimization with non-dominated sorting has been used for
community detection. The authors have considered complex structures of networks and used more than
objective functions for calculating the quality of community detection. The approach is competitive in terms
of static community. However, the authors have ignored the evolving aspect in their calculation which is
essential to identify a community in evolving nature. In the work of [17] an evolutionary genetic searching
Algorithm was developed for community detection with using two objective functions: the first one indicates

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Int J Elec & Comp Eng, Vol. 11, No. 5, October 2021: 4502 - 4512
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to the quality of CD or the modularity and the second one indicates to the quality of the smoothness defined
by temporal change (TC). The proposed genetic with mutation and migration operator which provides better-
searching capability in terms of exploration and exploitation. However, there was no incorporation to the
crowding distance CDIST which plays a crucial role in the exploration to provide more exploration which
leads to diverse Pareto front.
Overall, it is observed from the literature that the majority of the work that has been proposed for
solving community detection has not paid attention to the smoothness of the community solution concerning
time. Some approaches have used NMI as an indicator of the smoothness between two consecutive time
steps; however, they have not solved using non-dominated sorting which assures avoidance of local minima
that occur when the weighted average is used for solving it. The contribution of the research is given is being
as; i) It presents a novel framework for dynamic community detection in evolving social network with multi-
objective awareness; ii) It uses famous non-dominated sorting with crowding distance genetic Algorithm
NSGA-II for solving the problem of dynamic community detection; iii) It generates all multi-objective
evaluation metrics and it compares with the classical multi-objective genetic approach.
3. METHODOLOGY
This section presents the developed methodology for accomplishing dynamic aware community
detection system with multi-objective perspective. We present the problem formulation in sub-section 3.1.
Next, the framework of optimization in sub-section 3.2. Next, present the solution encoding in subsection
3.3. Afterwards, provide objective functions in sub-section 3.4. Next, present the Algorithm of non-
dominated sorting based community detection with dynamical awareness (NDS-CD-DA) in sub-section 3.5.
Lastly, the evaluation metrics that are used for comparison are given in sub-section 3.6.
3.1. Problem formulation
Assume we have an evolutionary social network 𝑆𝑁 = {𝑆𝑁1, 𝑆𝑁2, … . 𝑆𝑁𝑇} where 𝑆𝑁𝑡 represents the
social network at moment 𝑡. Our goal is to provide a dynamic community detection (DCD) system that takes
the 𝑆𝑁 at moment 𝑡 and provide the community detection at moment 𝑡 that will be given as 𝐺 =
{𝐺1, 𝐺2, … . 𝐺𝑇} where 𝐺𝑡 denotes the community decision at moment 𝑡. Hence, 𝑆𝑁𝑡 = (𝑉𝑡, 𝐸𝑡) where 𝑉𝑡
denotes the vertices and 𝐸𝑡 denotes the edges and 𝐺𝑡 = {𝑔𝑡
1
, 𝑔𝑡
2
, … 𝑔𝑡
𝑛
} where 𝑛 = |𝑉𝑡| and 𝑔𝑡
𝑗
∈ {1,2, … 𝐶𝑁𝑡}
where 𝐶𝑁𝑡 denotes the number of detected communities at moment t. Our goal is to provide the decision 𝐺 in
general with maintaining two aspects of quality: the snapshot quality represented by modularity which needs
to be maximized and the quality of the temporal change which is represented by NMI and it needs to also
maximize. Hence, our problem is a multi-objective optimization problem. The first objective is the
modularity at moment 𝑡 which is 𝑄𝑡and is given by (1):
𝑄𝑡 = ∑ [
𝑙𝑠
𝑚
− (
𝑑𝑠
2𝑚
)2
]
𝐶𝑁𝑡
𝑠=1 (1)
where 𝑙𝑠 Denotes the number of edges in the community𝑠; 𝑑𝑠 denotes the sum of the degree of nodes inside
the community 𝑠. The second objective is the normalized mutual information NMI between two partitions 𝐴
and 𝐵 measure that is given by (2):
𝑁𝑀𝐼 =
−2 ∑ ∑ 𝑐𝑖𝑗 log(𝐶𝑖𝑗 .|𝑉| ∕ 𝐶𝑖 .𝐶𝑗)
𝑘 𝐵
𝑗=1
𝑘 𝐴
𝑖=1
∑ 𝑐𝑖 log(𝐶𝑖./ |𝑉|)+∑ 𝑐𝑗 log (𝐶𝑗 .|𝑉| )
𝑘 𝐵
𝑗=1
𝑘 𝐴
𝑖=1
(2)
where 𝑘 𝐴 denotes the number of communities in 𝐴 ; 𝑘 𝐵 denotes the number of communities of 𝐵 ; 𝐶
denotes the confusion matrix whose element 𝑐𝑖𝑗 is a count of the number of nodes that belong to both the; 𝑖𝑡ℎ
a community of partition 𝐴 and 𝑗𝑡ℎ
a community of partition 𝐵 ; 𝐶𝑖 denotes the summation of the elements of
𝐶 in the row 𝑖 ; 𝐶𝑗 denotes the summation of the elements of 𝐶 in the row 𝑗 ; and |𝑉| denotes the number of
nodes in a network.
Assuming that 𝑢 = (𝑢1, 𝑢2) = (𝑁𝑀𝐼, 𝑀𝑜𝑑𝑢𝑙𝑎𝑟𝑖𝑡𝑦) our goal is to is accomplish more dominance
that is defined by the definition A. The challenges that are related to this problem can be summarized by the
handling of the high dynamic that is related the evolving of the social network which leads to the need of
trade-off between the smoothness of the decision of community detection with respect to time and the quality
of the proposed community detection from stand-alone clustering perspective. Another issue is the high
number of candidate solutions or the huge solution space which requires considerable running iterations and
solutions for convergence. However, it is also important to consider the continuous change in the social

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network which restricts that. Definition A (Pareto Dominance): a vector 𝑢 = (𝑢1, 𝑢2, … 𝑢𝑚) dominates
another vector 𝑣 = (𝑣1, 𝑣2, … 𝑣𝑚) (denoted by 𝑢 ≼ 𝑣) ) if and only if 𝑢 is partially less than 𝑣, 𝑖, 𝑒, ∀𝑖 ∈
{1,2, … . , 𝑚}, 𝑢𝑖 ≤ 𝑣𝑖 ∧ ∃𝑖 ∈ {1,2, … 𝑚}: 𝑢𝑖 < 𝑣𝑖.
3.2. Framework of optimization
The optimization that will perform community detection is presented under the framework of
Figure 1. As we see in the Figure, the framework is combined with the social network as input and
community partitioned social network CPSN as output. There are four essential blocks: the community
detection (CD) block, the modularity block, normalization mutual information (NMI) block, and multi-
objective optimization (MOO) block. The community detection provides the community partitioned social
network based on two inputs: the social network and the decision provided by the MOO. The MOO block
provides the searching with the assist of the CD based on reading two objective functions values, namely,
modularity and NMI. NMI is calculated based on two inputs: 〖CPSN〗_t and 〖CPSN〗_(t-1) which is
calculated based on the memory block.
Figure 1. The framework of multi-objective NSGA-II community detection with dynamical awareness,
CD=community detection, CPSN=community partitioned social network, MOO=multi-objective
optimization, 𝑧−1
memory with lag 1
3.3. Solution encoding
The goal of the solution encoding is to enable conducting the optimization based on defining an
appropriate solution space. Based on the problem formulation that was presented in section 3.1. For a social
network, 𝑆𝑁𝑡 = (𝑉𝑡, 𝐸𝑡) where 𝑉𝑡 = {𝑛𝑖
𝑡
}, 𝑖 = 1, … 𝑁𝑡, 𝐸𝑡 = {𝑒𝑗
𝑡
}, 𝑗 = 1,2, .. |𝐸𝑡|. Assuming that the
maximum of candidate communities in the social network at moment 𝑡 is 𝐶𝑁𝑡. Hence, the solution will be
𝑥 ∈ [1, 𝐶𝑁𝑡]𝑁𝑡.
𝑥 = [𝑥𝑘], 𝑘 = 1,2. . 𝑁𝑡 and 1≤ 𝑥𝑘 ≤ 𝐶𝑁𝑡.
3.4. Objective functions
Each solution 𝑥 of the solution space is evaluated according to the two objectives. And our goal is to
find the set of non-dominated solutions or Pareto front 𝑃𝐹 for the two objective functions.
𝑓1(𝑥) = 𝑄𝑡(𝑥, 𝑆𝑁𝑡)
𝑓2(𝑥) = 𝑁𝑀𝐼(𝑥, 𝑆𝑁𝑡, 𝑆𝑁𝑡−1)
3.5. Non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA)
This section presents the algorithm of NDS-CD-DA. The input of the algorithm is the social
network 𝑆𝑁𝑡 = {𝑆𝑁1, 𝑆𝑁2, … . 𝑆𝑁𝑇} and the output of the algorithm is the Pareto fronts of each of the
solutions of community detection of the social networks 𝑃𝐹 = {𝑃𝐹1, 𝑃𝐹2, … 𝑃𝐹𝑇}. Hence, in each of the
iterations, the algorithm will run NSGA-II on the corresponding social network and the result will be the
Pareto front. The dynamic awareness is obtained from maximizing the normalized mutual information NMI
between the community result of the current social network and the previous social network. The
Algorithm 1 runs for several iterations equals to T. At each iteration, it determines the number of nodes in the
social network as it is given in line number 7.1. Next, it creates the boundary of the searching accordingly as
it is given inline 7.3. Besides, it sets a different solution dimension according to the maximum potential

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number of communities in the network as it is given in line number 7.2. Afterwards, modularity and NMI are
used as two objective functions for running the algorithm.
Algorithm 1. NSGA-II based community detection for evolving social network
Input
𝑆𝑁𝑡 = {𝑆𝑁1, 𝑆𝑁2, … . 𝑆𝑁𝑇} //evolving social network
MaxCom, // the maximum number of communities in the network
maxIterations
numberofSolutions
objectiveFuntions=[@Modularity, @NMI]
Output
𝑃𝐹 = {𝑃𝐹1, 𝑃𝐹2, … 𝑃𝐹𝑇} // evolving pareto front
Start
1 for t=1 until T
1.1 N[1]=numberOfNodes(SN(t))
1.2 solutionSpaceBoundary=[1 MaxCom]
1.3 solutionSpaceDimension=N[1]
1.4 objectiveFuntions=[@Modularity,@NMI]
1.5 initateNSGAII(solutionSpaceBoundary,solutionSpaceDimension);
1.6 PF[1]=callNSGAIICommunities(maxIterations,numberofSolutions
,objectiveFuntions);
End
End
We present the pseudocode of NSGA-II in Algorithm 2. It accepts the number iterations
maxIterations, the number of solutions numberofSolutions, the number of objective functions
objectiveFuntions, the parameter of crossoverRate, the parameter of mutationRate. The output is given in
ParetoFront. At each iteration, the Algorithm performs an evaluation of the entire population using the
function evaluatePop. Also, it performs the non-dominated sorting using NonDominatedSorting(). After
performing the non-dominated sorting, the algorithm selects the parents and generated the off-spring as it is
given in lines number 8.3 and 8.4 consequently. Next, the parents and offspring are combined to produce the
extended population as it is given in 8.5. The extended population is evaluated using the objective functions
and it is used to do non-dominated sorting in lines number 8.6 and 8.7 consequently. Afterwards, we perform
the selection giving first priorities to the most dominating ranks and second priorities to the highest crowding
distance as it is given in lines number 8.8 and 8.9 consequently.
Algorithm 2. Pseudocode of non-dominating sorting genetic Algorithm NSGA-II used for community
detection
Input
maxIterations
numberofSolutions
objectiveFuntions
crossoverRate
mutationRate
Output
ParetoFront
Start
1 Pop=init(numberofSolutions)
2 for iter=1 until maxIterations
2.1 objValues=evaluatePop(Pop,@objectiveFuntions)
2.2 Ranks=NonDominatedSorting(Pop,objValues)
2.3 Parents=select(numberofSolutions,Ranks)
2.4 OffSpring=generateOffSpring(Parents,crossoverRate,mutationRate)
2.5 extendedPop=combine(Parents,OffSpring)
2.6 objValues=evaluatePop(extendedPop,@objectiveFuntions)
2.7 Ranks=NonDominatedSortiong(extendedPop,objValues)
2.8 [NextGeneration,Remaining]=select(numberofSolutions,Ranks)
2.9 NextGeneration=selectCrowdingDistance(NextGeneration,Remaining)
2.10 Pop=NextGeneration
End
3 ParetoFront=ExtractPareto(NextGeneration)
End
3.6. Dataset
This section presents the datasets that are used for evaluation. We use two real datasets Last.fm and
Douban as well as one synthetic dataset named SYNFIX:
a. Last.fm: it was released in the framework of the 2nd international workshop on information heterogeneity
and fusion in recommender systems [25]. It's consisted of more than 40 million active clients distributed

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in more than 190 countries. Last.fm dataset allows the clients listing online music and allow the clients to
extend the friendly relations with other clients. This dataset only records the listening of each client for
particular artists.
b. Douban: is a social website designed in 6/3/2005 in hina, it provides books, music and movies
recommendation based on user rating and evaluation. it's like Facebook where user can have friendship
relation through email communication. the rating is secret range from 1/5 which is useless to the 5/5
which is most useful [26].
c. SYNFIX: is a dynamic dataset which contains into 128 nodes totally [17]. It is divided into 4
communities each one has 32 nodes. the average degree of each node is 16 degree and shares zout edges
with nodes in other communities. The community structures become weaker as zout declines. Three
nodes randomly selected for the simulate evolving community structures to assigned to other
communities at each time. The number of edges that are shared with other nodes is 3. SYN-FIX dynamic
network is generated over 10 iterations. (two variants were used SYN-FIX3 and SYN-FIX5).
Last.fm and Douban are not evolving social network. Hence, we use Algorithm 3 for converting the
social network to the evolving network. The Algorithm takes the social network and the time of evolving as
inputs and provides the evolving social network as output. At each iteration, the algorithm selects some nodes
randomly breaks some of their links and adds new links.
Algorithm 3. Converting non-evolving social network to evolving social network
Input
Social network
Time of evolving
Output
Evolving social network
Start
1 For t=1 until the end of the time
1.1 Generate random number for the node
1.2 Generate random number for candidate links
1.3 Generate a random number of existing links
1.4 Break the generated existing link
1.5 Connect the generated candidate link
1.6 Save a snapshot of the network and keep it for the next iteration
End
End
3.7. Evaluation metrics
The other name is set coverage or C-metric which compares the Pareto sets 𝑃𝑠1 and 𝑃𝑠2 as in (3):
𝐶(𝑃𝑠1, 𝑃𝑠2) =
|{𝑦∈𝑃𝑠2|Ǝ𝑥∈𝑃𝑠1:𝑥≺𝑦}|
𝑃𝑠2
(3)
𝐶 Equals the ratio of non-dominated solutions in Ps2 that are dominated by non-dominated solutions in Ps1,
to the number of solutions in𝑃𝑠2. Thus, when evaluating a set Ps, it is important to minimize the value of
𝐶(𝑋; 𝑃𝑠) for all Pareto sets 𝑋.
4. EVALUATION AND EXPERIMENTAL WORK
This section presents the experimental work and evaluation of the developed NDS-CD-DA and its
comparison with the benchmark DECS cAs it is been shown in Figure 2, NDS-CD-DA has accomplished a
domination percentage of 100% over DECS for almost all iterations. This is clearer in Lastfm dataset and is
only less with 10% for moment two of the social network evolution in Douban dataset. This interpreted by
the enabling of non-domination awareness of searching that exists in NDS-CD-DA comparing with weighted
sum evaluation of DECS. Table 1 represent the parameter names and the value used in the proposed work.
The evaluation of community detection is decomposed into two parts: the first one is the self-
validation of the progress of our NDS-CD-DA algorithm. It shows the evolution of the social network with
two graphs: the first one is the NMI metric and the second one is the modularity metric while the Algorithm
is doing the searching. We presented that in Figures 3 until 5 for Lastfm, Douban and SYNFIX respectively.
The second one is to compare the set coverage of NDS-CD-DA over DECS for 10 iterations of the
evolution of the social network and it is given in Figures 2 for Lastfm and Douban, and in Figure 6 for
SYNFIX. We observe that in the entire evolution of the social network, NDS-CD-DA has a positive set
coverage over DECS and reaching one in most cases. Hence, it can be said that NDS-CD-DA has more
capability of attaining higher modularity and NMI over DECS.

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Figure 2. Set coverage for NDS-CD-DA over DESC for two datasets: Lastfm to the left and the Douban to
the right, for 10 iterations
Table 1. Parameters of NDS-CD-DA
Parameter name Value
Number of iterations 200
Number of solutions 100
Maximum number of communities 50
Figure 3. The evolution of the searching in the social network for communities considering the iterations of
genetic and the time of evolving for Lastfm

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genetic and the time of evolving for Douban
genetic and the time of evolving for FIX

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The Figures 2 and 6 shows that using single objective optimization for community detection with
only focusing on the modularity as a metric for decomposing the social network into sub-groups named
communities leads to falling in sub-optimality. The reason is that the performance needs not only to provide
the community but also to smooth the decision of the optimization which might have a conflicting nature.
Hence, using the result of multi-objective optimization concerning the two objectives lead to higher
optimality. This is why we see that for all snapshots of the social network in the Figures NDS-CD-DA is
dominating over DESC.
Figure 6. Set coverage for NDS-CD-DA over DESC for two datasets SYNFIX (two variants were used syn-
fix3 and syn-fix5) for 10 iterations
5. CONCLUSION
This work has presented a novel community detection approach based on famous multi-objective
optimization Algorithm named non-dominated sorting genetic algorithm NSGA-II. It is designated as non-
dominated sorting based community detection with dynamical awareness NDS-CD-DA. It uses two objective
functions: The first one is the modularity which indicates to quality of community detection one single
snapshot of the social network and the second one is the NMI which indicates to the quality of community
detection concerning the temporal change of the social network. The implementation of the approach was
done on several social networks data; namely, dataset Lastfm, Douban, SYNFIX of that are evolving
network. Lastfm, Douban one is snapshot datasets that have been converted to evolving using an approach
that has been presented. The results reveal superiority in the performance in terms of the domination aspect
which is represented by the set coverage metric. The limitation is that the proposed optimization consumes a
considerable amount of iterations before convergence. Future work first is to reduce the number of iterations
by starting from random population close to the last search population assuming that the dynamic is not high
in the evolving of the social network, second is to incorporate this model in other useful systems such as the
recommendation system and the implicit link predictions.
ACKNOWLEDGEMENTS
This work is supported by the ministry of higher education (MOHE) under the fundamental research
grant scheme (FRGS) reference code FRGS/1/2018/ICT04/UTHM/02/3.
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 ISSN: 2088-8708
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BIOGRAPHIES OF AUTHORS
Muhammed. E Abd Alkhalec Tharwat is currently a PhD candidate in information technology,
FSKTM, UTHM, Batu Pahat, Johor, Malaysia. his research interest includes data mining,
information retrieval, social network analysis, link prediction Algorithms, community detection,
recommendation system, and machine learning.
Mohd Farhan Md Fudzee is an Associate Professor at Fakulti Sains Komputer dan Teknologi
Maklumat (FSKTM), University Tun Hussein Onn Malaysia, Malaysia. He obtained a PhD from
Deakin University, Australia. His research interests include multimedia computing, green
computing, decision-making, web technology, and e-government.
Shahreen Kasim is currently an associate professor in the Department of Security Information and
Web Technology, Faculty of Computer Science and Information Technology, Universiti Tun
Hussein Onn Malaysia. Her areas of interest include bioinformatics, soft computing, data mining,
web and mobile application.
Azizul Azhar Ramli is currently an Associate Professor at Fakulti Sains Komputer dan Teknologi
Maklumat (FSKTM), University Tun Hussein Onn Malaysia, Malaysia. He obtained a PhD
(Management Engineering), IPS, Waseda University, Japan. His research interests include data
mining, soft computing, and machine learning.
Mohammed. Q Ali is M.Eng. in communications engineering from UTHM, Johor, Malaysia, and
his research contain WSN, security network, attack on the network and sinkhole attack.

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