scholarly journals Predicting Attributes of Nodes Using Network Structure

2021 ◽  
Vol 12 (2) ◽  
pp. 1-23
Author(s):  
Sarwan Ali ◽  
Muhammad Haroon Shakeel ◽  
Imdadullah Khan ◽  
Safiullah Faizullah ◽  
Muhammad Asad Khan
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Pairwise Interaction ◽  
Social Selection ◽  
Complex Interdependence ◽  
Specific Attribute ◽  
Node Attribute ◽  
Regression Algorithms ◽  
Node Attributes ◽  
Real World Datasets

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a , using all attributes of its h -hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a . We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Morality-Driven Data Forwarding With Privacy Preservation in Mobile Social Networks

2012 ◽  
Vol 61 (7) ◽  
pp. 3209-3222 ◽  
Author(s):  
Xiaohui Liang ◽  
Xu Li ◽  
Tom H. Luan ◽  
Rongxing Lu ◽  
Xiaodong Lin ◽  
...  
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Mobile Social Networks ◽  
Data Forwarding

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

scholarly journals Learning Diffusion Probability Based on Node Attributes in Social Networks

2011 ◽  
pp. 153-162 ◽  
Author(s):  
Kazumi Saito ◽  
Kouzou Ohara ◽  
Yuki Yamagishi ◽  
Masahiro Kimura ◽  
Hiroshi Motoda
Keyword(s):  
Social Networks ◽  
Node Attributes

Privacy in Online Social Networks

2021 ◽  
pp. 2078-2109
Author(s):  
Ramanpreet Kaur ◽  
Tomaž Klobučar ◽  
Dušan Gabrijelčič
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Mitigation Strategies ◽  
Research Issues ◽  
Privacy Requirements ◽  
Privacy Threats

This chapter is concerned with the identification of the privacy threats to provide a feedback to the users so that they can make an informed decision based on their desired level of privacy. To achieve this goal, Solove's taxonomy of privacy violations is refined to incorporate the modern challenges to the privacy posed by the evolution of social networks. This work emphasizes on the fact that the privacy protection should be a joint effort of social network owners and users, and provides a classification of mitigation strategies according to the party responsible for taking these countermeasures. In addition, it highlights the key research issues to guide the research in the field of privacy preservation. This chapter can serve as a first step to comprehend the privacy requirements of online users and educate the users about their choices and actions in social media.

Sign in / Sign up

Export Citation Format

Share Document