M. LOUNNAS Bilal

MCA

Directory of teachers

Department

Informatics Department

Research Interests

Data mining Bioinformatics Information retrival Pattern matching

Contact Info

University of M'Sila, Algeria

Email : bilal.lounnas@univ-msila.dz

On the Web:

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Recent Publications

2024-07-03

A robust two-step algorithm for community detection based on node similarity

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities.
Citation

M. LOUNNAS Bilal, (2024-07-03), "A robust two-step algorithm for community detection based on node similarity", [national] The Journal of Supercomputing , Springer

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2022

A complex network community detection algorithm based on random walk and label propagation

The community structure is proving to have a very important role in the understanding of
complex networks, but discovering them remains a very diÕcult problem despite the
existence of several methods. In this article, we propose a novel algorithm for discovering
communities in complex networks based on a modiÒed random walk (RW) and label
propagation algorithm (LPA). First, we calculate the similarity between nodes based on the
new formula of RW. Then, the labels are propagated by the obtained similarity of the Òrst
step using LPA. Finally, the third step will be a new measure to Ònd the optimal partitioning
of communities. Experimental results obtained on several real and synthetic networks
reveal that our algorithm outperforms existing methods in Ònding communities.
Citation

M. LOUNNAS Bilal, (2022), "A complex network community detection algorithm based on random walk and label propagation", [national] Transactions on Emerging Telecommunications Technologies. , Wiley

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