Link Prediction in Online Social Networks Based on the Unsupervised Marginalized Denoising Model

Hao, Zhangang

doi:10.1109/access.2019.2912662

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…It assigns a rank to every node in the network. [21]focused on an unsupervised model to consider LP problem as a matrix denoising (MD) problem. Topological structure of the social graph is used in designing a mapping function that maps the existing network to a network consisting of all the links.…”

Section: A Traditional Approachmentioning

confidence: 99%

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Behera

Das

Swetanisha

et al. 2021

Wireless Pers Commun

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The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

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Section: A Traditional Approachmentioning

confidence: 99%

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Behera

Das

Swetanisha

et al. 2021

Wireless Pers Commun

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“…Sheikh et al [15] introduce the GAT2VEC framework that generates structural contexts by structural information, and generates attribute contexts by attributes, and employs a shallow neural network model to learn a joint representation from them. Most attribute-based methods are more efficient in terms of computation time [16]. As there are many different types of attribute information, it is difficult to give a general solution.…”

Section: Introductionmentioning

confidence: 99%

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

et al. 2020

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The link prediction can be used to seek missing or future links in the network, so it has become a hot research topic. The network generally contains two types of information: the topological structure of network formed by the connection between nodes, and the attribute information of nodes. However, the existing topology-based link prediction algorithms consider little attribute information. In this paper, a novel algorithm called Network Embedding with Attribute Deep Fusion for Link Prediction (NEADF-LP) is proposed. We get the embedded vectors with topological structure and attribute information by structure encoder and attribute encoder respectively, and fuse two vectors deeply. Compared with mainstream baselines on CiteSeer and Cora datasets, the results show that the deep fusion of topological structure and attribute information improve the accuracy of link prediction effectively. INDEX TERMS Complex network, deep fusion, link prediction, network embedding.

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“…Notwithstanding this definition, the same idea applies to many different situations, such as recommendation systems ( 25 ), bioinformatics ( 26 ), scientific collaboration networks ( 27 ), criminal networks ( 28 ), or even estimating the reliability of network data ( 29 ), to name a few. In the case of online social networks, link prediction has been considered, for example, by Song et al ( 30 ) or Hao ( 31 ). (See ref.…”

mentioning

confidence: 99%

Triadic influence as a proxy for compatibility in social relationships

Ruiz-García

Ozaita

Pereda

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric—the “triadic influence”—that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks—functions of the triadic influence and the personalities of the students—control the evolution of real social networks, opening an avenue for the quantitative study of these systems.

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Link Prediction in Online Social Networks Based on the Unsupervised Marginalized Denoising Model

Cited by 5 publications

References 42 publications

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

The Deep Fusion of Topological Structure and Attribute Information for Link Prediction

Triadic influence as a proxy for compatibility in social relationships

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