Supervised local community detection algorithm Online publication date: Tue, 16-Feb-2021
by Ali Choumane; Abbass Al-Akhrass
International Journal of Data Science (IJDS), Vol. 5, No. 3, 2020
Abstract: Community detection aims to partition a network into internally densely connected groups of nodes. In huge networks, exploring the whole network is computationally very expensive. For applications such as antiterrorism, the spread of disease on networks and viral advertising, researchers are now more interested to find the community surrounding one or a few individuals. In this context, we propose a local community detection algorithm that starts from a seed node and iteratively expands it to reach the community that resembles the most to a real-life community. The expansion process is controlled by a neural network classifier that decides which nodes to add to the community being expanded. This classifier is built using three features quantifying the strength of relationships between nodes and communities. Experiments achieved on Lancichinetti-Fortunato-Radicchi (LFR) synthetic networks and real-world networks from different application domains, proved the high performance of our method as compared to the baselines.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook