scholarly journals Distributed Community Detection based on Apache Spark using Multi Label Propagation for Digital Social Networks

2018 ◽  
Vol 7 (4.5) ◽  
pp. 79
Author(s):  
Satya Keerthi Gorripati ◽  
Valli Kumari
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Apache Spark ◽  
Community Group ◽  
Community Based ◽  
Data Set ◽  
Interactive Mode ◽  
Community Detection Algorithm

Organization, Government and Individual (OGI) have popularized the use of Digital Social Networks (DSN) that reduces the processing time of social-aware tasks. To accomplish a community-based communication, each social-aware task should identify its community group. The identified group uses a task to avail all the DSN benefits to their customers / citizens. As a result, the community-based detection algorithm has played a significant role in literature. However, the existing algorithms have had several challenging issues, such as performance and scalability. Thus, a distributed community detection algorithm is presented using Apache Spark’s Resilient Distributed Data Set (RDD) framework based on the Scala programming language. The Apache Spark framework provides an ideal solution that offers ease of coding, performance, interactive mode and disk Input-Output bottlenecks in Hadoop /Map Reduce. Besides, it presents a platform of distributed community detection that reduces the computational computation by applying transformations, aggregations and joins. The experimental results show that the proposed framework achieves high accuracy for both real-world and synthetic networks.  

Node Trust: an effective method to detect non-overlapping community in social networks

2020 ◽  
pp. 2150036
Author(s):  
Jinfang Sheng ◽  
Qiong Li ◽  
Bin Wang ◽  
Wanghao Guan ◽  
Jinying Dai ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Community Detection ◽  
Linear Time ◽  
Deep Understanding ◽  
Detection Algorithm ◽  
Underlying Structure ◽  
Community Detection Algorithm ◽  
Stable Community ◽  
Synthetic Networks

Social networks are made up of members in society and the social relationships established by the interaction between members. Community structure is an essential attribute of social networks. The question arises that how can we discover the community structure in the network to gain a deep understanding of its underlying structure and mine information from it? In this paper, we introduce a novel community detection algorithm NTCD (Community Detection based on Node Trust). This is a stable community detection algorithm that does not require any parameters settings and has nearly linear time complexity. NTCD determines the community ownership of a node by studying the relationship between the node and its neighbor communities. This relationship is called Node Trust, representing the possibility that the node is in the current community. Node Trust is also a quality function, which is used for community detection by seeking maximum. Experiments on real and synthetic networks show that our algorithm has high accuracy in most data sets and stable community division results. Additionally, through experiments on different types of synthetic networks, we can conclude that our algorithm has good robustness.

Leader‐based community detection algorithm for social networks

2017 ◽  
Vol 7 (6) ◽  
Author(s):  
Nivin A. Helal ◽  
Rasha M. Ismail ◽  
Nagwa L. Badr ◽  
Mostafa G. M. Mostafa
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Algorithm ◽  
Community Detection Algorithm

scholarly journals A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Dongqing Zhou ◽  
Xing Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Detection Problem ◽  
Signed Networks ◽  
Discrete Pso ◽  
Community Detection Algorithm ◽  
Definition Of ◽  
The Impact

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

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