scholarly journals A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

2021 ◽  
Author(s):  
Mehrdad Rostami ◽  
Mourad Oussalah
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Feature Weighting ◽  
Label Propagation ◽  
Second Phase ◽  
Node Centrality ◽  
Propagation Algorithm

Abstract Community detection is one of the basic problems in social network analysis. Community detection on an attributed social networks aims to discover communities that have not only adhesive structure but also homogeneous node properties. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, a novel attributed community detection method through an integration of feature weighting with node centrality techniques is developed in this paper. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state of the art methods and ascertain the effectiveness of the developed method for attributed community detection.

scholarly journals A Label Propagation Based User Locations Prediction Algorithm in Social Network

2020 ◽  
pp. 165-174
Author(s):  
Huan Ma ◽  
Wei Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Label Propagation ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Network Graph ◽  
Propagation Algorithm ◽  
Node Location ◽  
User Location

AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.

scholarly journals A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Mu Zhu ◽  
Mengyu Shi ◽  
...  
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Detection Method ◽  
Label Propagation ◽  
Experimental Results ◽  
Superior Performance ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Poor Stability

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

scholarly journals Differentially Private Recommendation System Based on Community Detection in Social Network Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Gesu Li ◽  
Zhipeng Cai ◽  
Guisheng Yin ◽  
Zaobo He ◽  
Madhuri Siddula
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Method ◽  
Personal Information ◽  
The Internet ◽  
Sensitive Information ◽  
Network Applications ◽  
Target Attributes

The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

scholarly journals NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 260 ◽  
Author(s):  
Bingyang Huang ◽  
Chaokun Wang ◽  
Binbin Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Label Propagation ◽  
Detection Methods ◽  
Overlapping Communities ◽  
Propagation Algorithm ◽  
Pruning Strategy ◽  
Node Similarity ◽  
Attributed Networks

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

A label propagation approach based on local optimization

2018 ◽  
Vol 29 (06) ◽  
pp. 1850047
Author(s):  
Xiaohong Zhang ◽  
Yulin Jiang ◽  
Jianji Ren ◽  
Chaosheng Tang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Large Scale ◽  
Local Optimization ◽  
Label Propagation ◽  
Propagation Mechanism ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Special Factors

Community detection offers an important way to understand the structures and functions of social network. The label propagation algorithm has attracted vast attention since it is very suitable for discovering communities from large-scale networks. However, the algorithm suffers from the instability and inefficiency problem caused by the random policies it adopted. In this paper, we propose a novel label propagation approach based on local optimization to deal with the problem. The approach introduces a pre-propagation mechanism to optimize randomly initialized labels according to special factors, for example, node compactness. After that, it traverses and relabels nodes in the descending order of aggregate influence. The experiment results demonstrate the usefulness and effectiveness of our approach.

An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm

2017 ◽  
pp. 1062-1075 ◽  
Author(s):  
Khaled Ahmed ◽  
Aboul Ella Hassanien ◽  
Ehab Ezzat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Label Propagation ◽  
Optimization Approach ◽  
Detection Problem ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

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