A PSO Based Community Detection in Social Networks with Node Attributes

2018 ◽  
Author(s):  
K Chaitanya ◽  
Dvln Somayajulu ◽  
P Radha Krishna
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Node Attributes

scholarly journals Clustering attributed graphs: Models, measures and methods

2015 ◽  
Vol 3 (3) ◽  
pp. 408-444 ◽  
Author(s):  
CECILE BOTHOREL ◽  
JUAN DAVID CRUZ ◽  
MATTEO MAGNANI ◽  
BARBORA MICENKOVÁ
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Social Systems ◽  
Graph Clustering ◽  
Literature Survey ◽  
Clustering Methods ◽  
Open Problems ◽  
Attributed Graphs ◽  
Partial Representation ◽  
Node Attributes

AbstractClustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models asattributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing, and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

scholarly journals Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

2017 ◽  
Vol 10 (4) ◽  
pp. 50
Author(s):  
Bikash Chandra Singh ◽  
Mohammad Muntasir Rahman ◽  
Md Sipon Miah ◽  
Mrinal Kanti Baowaly
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Online Social Network ◽  
Detection Methods ◽  
Structural Patterns ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Node Attributes ◽  
Overlapping Community

Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

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