ArticleRoyal Society open science2017
Link prediction in multiplex online social networks.
Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Reconstruction of Multiplex Networks with Correlated Layers.Entropy (Basel, Switzerland) · 2026Article
- Modeling roles and trade-offs in multiplex networks.Nature communications · 2026Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- Article
- Enhanced link prediction using sentiment attribute and community detection.Journal of ambient intelligence and humanized computing · 2023Article
- A New Strategy in Boosting Information Spread.Entropy (Basel, Switzerland) · 2022Article
- Discrimination reveals reconstructability of multiplex networks from partial observations.Communications physics · 2022Article
- A roadmap towards predicting species interaction networks (across space and time).Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- An information theoretic approach to link prediction in multiplex networks.Scientific reports · 2021Article
- Seven-Layer Model in Complex Networks Link Prediction: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Link prediction in real-world multiplex networks via layer reconstruction method.Royal Society open science · 2020Article
- A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks.Frontiers in genetics · 2020Article
- Application of hyperbolic geometry in link prediction of multiplex networks.Scientific reports · 2019Article
- Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer's Disease.Entropy (Basel, Switzerland) · 2019Article
- Quantifying layer similarity in multiplex networks: a systematic study.Royal Society open science · 2018Article
- Information Spread and Topic Diffusion in Heterogeneous Information Networks.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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%.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.