Analysis of Link Prediction Method in Mobile Social Network

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

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An Enhancement on Mobile Social Network using Social Link Prediction with Improved Human Trajectory Internet Data Mining

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2019.0100318 ◽

2019 ◽

Vol 10 (3) ◽

Author(s):

B. Suryakumar ◽

Dr. E.

Keyword(s):

Data Mining ◽

Social Network ◽

Link Prediction ◽

Mobile Social Network ◽

Social Link

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Deep Reinforcement Learning Based on Link Prediction Method in Social Network Analysis

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1127.1292s219 ◽

2019 ◽

Vol 9 (2S2) ◽

pp. 820-826

Keyword(s):

Machine Learning ◽

Social Network ◽

Reinforcement Learning ◽

Supervised Learning ◽

Link Prediction ◽

Prediction Models ◽

Learning Strategy ◽

Prediction Method ◽

Gradient Boosting ◽

Data Set

Improving the performance of link prediction is a significant role in the evaluation of social network. Link prediction is known as one of the primary purposes for recommended systems, bio information, and web. Most machine learning methods that depend on SNA model’s metrics use supervised learning to develop link prediction models. Supervised learning actually needed huge amount of data set to train the model of link prediction to obtain an optimal level of performance. In few years, Deep Reinforcement Learning (DRL) has achieved excellent success in various domain such as SNA. In this paper, we present the use of deep reinforcement learning (DRL) to improve the performance and accuracy of the model for the applied dataset. The experiment shows that the dataset created by the DRL model through self-play or auto-simulation can be utilized to improve the link prediction model. We have used three different datasets: JUNANES, MAMBO, JAKE. Experimental results show that the DRL proposed method provide accuracy of 85% for JUNANES, 87% for MAMABO, and 78% for JAKE dataset which outperforms the GBM next highest accuracy of 75% for JUNANES, 79% for MAMBO and 71% for JAKE dataset respectively trained with 2500 iteration and also in terms of AUC measures as well. The DRL model shows the better efficiency than a traditional machine learning strategy, such as, Random Forest and the gradient boosting machine (GBM).

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Constructing the social network prediction model based on data mining and link prediction analysis

Library Hi Tech ◽

10.1108/lht-11-2018-0179 ◽

2019 ◽

Vol 38 (2) ◽

pp. 320-333

Author(s):

Yuxian Gao

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Link Prediction ◽

Prediction Method ◽

Analysis Method ◽

Data Set ◽

Content Type ◽

Community Mining ◽

The Relationship

Purpose The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis. Design/methodology/approach In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship. Findings The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship. Research limitations/implications Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data. Practical implications The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining. Originality/value This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.

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