Enhance Link Prediction in Online Social Networks Using Similarity Metrics, Sampling, and Classification

2018 ◽  
pp. 823-833 ◽  
Author(s):  
Pham Minh Chuan ◽  
Cu Nguyen Giap ◽  
Le Hoang Son ◽  
Chintan Bhatt ◽  
Tran Dinh Khang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Similarity Metrics

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

Multilayer Link Prediction in Online Social Networks

2018 ◽  
Author(s):  
Haris Mandal ◽  
Miroslav Mirchev ◽  
Sasho Gramatikov ◽  
Igor Mishkovski
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction

Link prediction measures considering different neighbors’ effects and application in social networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750033 ◽  
Author(s):  
Peng Luo ◽  
Chong Wu ◽  
Yongli Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Models ◽  
Network Evolution ◽  
Social Characteristics ◽  
Prediction Methods ◽  
Proposed Model ◽  
Social Network Evolution ◽  
Artificial Network

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

scholarly journals A Weighted path based Link Prediction in Social Networks using Bounded Length of Separation between Nodes

2018 ◽  
Vol 7 (4.10) ◽  
pp. 274
Author(s):  
Srilatha P ◽  
Manjula R
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Gene Network ◽  
Biological Network ◽  
Small World ◽  
Large Size ◽  
Connection Pattern ◽  
Structural Connections ◽  
Length Of Separation

The problem of link prediction in online social networks like facebook, myspace, Hi5 and in other domains like biological network of molecules, gene network to model disease have became very popular because of the structural connections and relationships  among the entities. The classical methods of link prediction based on the topological structure of the graph exploit all different paths of the network which are being computationally expensive for large size of networks. In this paper, incorporating  the small world phenomenon, the proposed algorithm traverses all the paths of bounded length by considering clustering information and the connection pattern of the edges as weights on the edges in the graph. As a result, the proposed algorithm will be able to predict accurately than the existing link prediction algorithms. Our analysis and experiment on real world networks shows that our algorithm outperforms other approaches in terms of time complexity and the prediction accuracy.  

scholarly journals Link prediction in multiplex online social networks

2017 ◽  
Vol 4 (2) ◽  
pp. 160863 ◽  
Author(s):  
Mahdi Jalili ◽  
Yasin Orouskhani ◽  
Milad Asgari ◽  
Nazanin Alipourfard ◽  
Matjaž Perc
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Support Vector ◽  
Svm Classifier ◽  
Multiplex Network ◽  
Average Accuracy ◽  
Potential Applications ◽  
Meta Path ◽  
Highly Correlated

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

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