scholarly journals Product Recommendation based on Sybil and Trusted Votes in Social Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 5434-5440
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Social Behaviour ◽  
Social World ◽  
Detection Algorithm ◽  
Information Gathering ◽  
Online Environment ◽  
The Social ◽  
Community Detection Algorithm

Networks is a platform which is easily accessible by normal users worldwide. Online Social Networks facilitates users online to get registered with ease of speed and create their own accounts to communicate with the social world for information gathering. This platform allows everyone to get registered online irrespective of their social behaviour. Users here are creating duplicate accounts that is creating Sybil in the network. By this Sybil online Social Networks are suffering for different kinds of Sybil attacks online. In social networks user’s feedback and preferences play an important role in suggesting friends online or recommending products online. When collecting the feedback or preferences of any product online both Sybil user’s and real user’s data is considered as we are not differentiating the Sybil user or real user. From this products, recommended online will not have an efficient rating which would divert the buyers online. To over this problem we propose Sybil Community Detection Algorithm (SCD) and TrustRank Algorithm that bifurcates real user votes and Sybil users votes to fetch the efficient products online thus build secure online environment.

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 Geographical Structural Features of the WeChat Social Networks

2020 ◽  
Vol 9 (5) ◽  
pp. 290
Author(s):  
Chuan Ai ◽  
Bin Chen ◽  
Hailiang Chen ◽  
Weihui Dai ◽  
Xiaogang Qiu
Keyword(s):  
Social Networks ◽  
Communication Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Statistical Models ◽  
Interaction Analysis ◽  
Structural Features ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Characteristics Analysis

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.

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