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

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.

2020 ◽  
Vol 10 (18) ◽  
pp. 6547
Author(s):  
Daniela Quiñones ◽  
Cristian Rusu ◽  
Diego Arancibia ◽  
Sebastián González ◽  
María Josée Saavedra

With the growth and overcrowding of the internet, the use of online social networks has been increasing. Currently, social networks are used by a wide variety of users–with different objectives and in different contexts of use–, so it is essential to design intuitive and easy to use social network applications that generate a positive user experience (UX). The heuristic evaluation is a well-known evaluation method that allows detecting usability problems; a group of experts evaluates a product and/or system using a set of heuristics as a guide. Although the heuristic evaluation is oriented to evaluate the usability, it can be useful to evaluate other aspects related to the UX. Due to the specific features of social networks, it is necessary to have a specific set of heuristics to evaluate them. Sets of specific heuristics for social networks have been proposed, but they focus on evaluating the only usability. This article presents a set of heuristics that attend not only usability issues, but other UX factors as well, social network user experience heuristics (SNUXH). The new set of heuristics was developed, validated, and refined in four iterations. The results obtained in the experimental validation indicate that the SNUXH set is useful and more effective than generic heuristics (Nielsen’s heuristics) when evaluating social networks.


2018 ◽  
Vol 7 (2.21) ◽  
pp. 398
Author(s):  
K M. Monica ◽  
R Parvathi

A trending issue in the network system that aids in learning and understanding the overall network structure is the community detection in the social network. Actually, they are the dividing wall which divides the node of the network into several subgroups. While dividing, the nodes within the subgroups will get connected densely but, their connections will be sparser between the subgroups. The ultimate objective of the community detection method is to divide the network into dense regions of the graph. But, in general, those regions will correlate with close related entities which can be then said that it is belonging to a community. It is defined based on the principle that the pair of nodes will be connected only if they belong to the same community and if they don’t share the communities, they are less likely to be connected. The vital problems across various research fields like the detection of minute and scattered communities have been necessitated with the ever growing variety of the social networks. The problem of community detection over the time has been recognized with the literature survey and the proposal methodology of set theorem to find the communities detection where the group belongs to activities. In addition to this, several basic concepts are stated in an exhaustive way where the research fields arise from social networks.  


2021 ◽  
Author(s):  
Mehrdad Rostami ◽  
Mourad Oussalah

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.


Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.


2016 ◽  
Vol 26 (3) ◽  
pp. 566-586 ◽  
Author(s):  
Xi Chen ◽  
Yin Pan ◽  
Bin Guo

Purpose – The purpose of this paper is to determine the influence and interaction of social networks and personality traits on the self-disclosure behavior of social network site (SNS) users. According to social capital theory and the Big Five personality model, the authors hypothesized that social capital factors would affect the accuracy and amount of self-disclosure behavior and that personality traits would moderate this effect. Design/methodology/approach – A survey was conducted to collect data from 207 SNS users. The questionnaire was administered in university classrooms and libraries and via e-mail. The measurement model and structural model were examined by using LISREL 8.8 and SmartPLS 2.0. Findings – Based on the path analysis, the authors identified several interesting patterns to explain self-disclosure behavior on SNSs. First, the centrality of SNS users has a positive effect on their amount of self-disclosure. Moreover, people who are more extroverted disclose personal information that is more accurate with the level of the cognitive dimension held constant and disclose a greater amount of personal information with the level of the structural dimension held constant. From a practical perspective, the results may provide useful insight for companies operating SNSs. Originality/value – This study analyzed the influence of social capital factors on SNS users’ self-disclosure, as well as the interactions between personality and social capital factors. Specifically, the authors examined six important variables of social capital divided into three dimensions. This research complements current research on SNSs by focusing on SNS users’ motivation to disclose self-related information in addition to information sharing.


2013 ◽  
Vol 8 (1.) ◽  
Author(s):  
Slavica Vrsaljko ◽  
Tea Ljubimir

SMS messaging and communicating on social networks are increasingly widespread forms of informal communication. Mobile phones have almost all, and in addition they open profiles on the Internet social network, corresponding in this way with their peers. In writing messages is being recorded a large number of spelling errors, most of errors are those whose adoption is foreseen in the the lower grades of elementary school. In order to determine the level of mastery of linguistic norms, the message will be analysed as well as comments from the social networks of fourth-grade students.


Author(s):  
Ling Wu ◽  
Qishan Zhang ◽  
Chi-Hua Chen

With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.


2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Feng ◽  
Qiang Li ◽  
Yuede Ji ◽  
Dong Guo ◽  
Xiangyu Meng

Online social networks have become an essential part of our daily life. While we are enjoying the benefits from the social networks, we are inevitably exposed to the security threats, especially the serious Advanced Persistent Threat (APT) attack. The attackers can launch targeted cyberattacks on a user by analyzing its personal information and social behaviors. Due to the wide variety of social engineering techniques and undetectable zero-day exploits being used by attackers, the detection techniques of intrusion are increasingly difficult. Motivated by the fact that the attackers usually penetrate the social network to either propagate malwares or collect sensitive information, we propose a method to assess the security risk of the user being attacked so that we can take defensive measures such as security education, training, and awareness before users are attacked. In this paper, we propose a novel user analysis model to find potential victims by analyzing a large number of users’ personal information and social behaviors in social networks. For each user, we extract three kinds of features, i.e., statistical features, social-graph features, and semantic features. These features will become the input of our user analysis model, and the security risk score will be calculated. The users with high security risk score will be alarmed so that the risk of being attacked can be reduced. We have implemented an effective user analysis model and evaluated it on a real-world dataset collected from a social network, namely, Sina Weibo (Weibo). The results show that our model can effectively assess the risk of users’ activities in social networks with a high area under the ROC curve of 0.9607.


Sign in / Sign up

Export Citation Format

Share Document