Link Prediction Based Minimum Cost and Balanced Partition of Large Online Social Networks

2019 ◽  
Author(s):  
Romas James Hada ◽  
Miao Jin ◽  
Ying Xie ◽  
Linh Le
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Minimum Cost ◽  
Balanced Partition

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.  

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