RankMerging: a supervised learning-to-rank framework to predict links in large social networks

  title={RankMerging: a supervised learning-to-rank framework to predict links in large social networks},
  author={Lionel Tabourier and Daniel Faria Bernardes and Anne-Sophie Libert and Renaud Lambiotte},
  journal={Machine Learning},
Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it… 
A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
A diagonally symmetrical supra-adjacency matrix is employed to represent the dynamic social networks, and a temporal links prediction framework combining with an improved gravity model is proposed.
Polarization in large socio-informational networks: models and measures
  • Computer Science
  • 2022
This doctoral project proposes to build on graph models for networks representing digital traces on large web platforms and the development of measures from information theory, to understand and understand polarization as a formal property of large complex networks, and the impact that recent Recommender Systems have on it.
Predicting interactions between individuals with structural and dynamical information
This work tackles the issue of activity prediction in link streams, that is to say predicting the number of links occurring during a given period of time and presents a protocol that takes advantage of the temporal and structural information contained in the link stream.
Learning Dynamic Network Models for Complex Social Systems
It is demonstrated that the holistic approach for modeling network dynamics in coevolving, multiplex networks outperforms factored methods that separately consider temporal and cross-layer patterns.
Using Massively Multiplayer Online Game Data to Analyze the Dynamics of Social Interactions
This chapter illustrates the versatility of the Travian dataset with case studies of how to analyze different aspects of social network dynamics, including the effects of aggression and cooperation on the network structure and modeling link formation patterns across network layers.


A Survey of Link Prediction in Social Networks
This article surveys some representative link prediction methods by categorizing them by the type of models, largely considering three types of models: first, the traditional (non-Bayesian) models which extract a set of features to train a binary classification model, and second, the probabilistic approaches which model the joint-probability among the entities in a network by Bayesian graphical models.
Supervised random walks: predicting and recommending links in social networks
An algorithm based on Supervised Random Walks is developed that naturally combines the information from the network structure with node and edge level attributes and outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.
Supervised rank aggregation approach for link prediction in complex networks
A new topological approach for link prediction in dynamic complex networks that applies a supervised rank aggregation method and shows the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database.
Link Prediction via Matrix Factorization
The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores, and may be combined with optional explicit features for nodes or edges, which yields better performance.
Link prediction using supervised learning
This research identifies a set of features that are key to the superior performance under the supervised learning setup, and shows that a small subset of features always plays a significant role in the link prediction job.
Supervised methods for multi-relational link prediction
A novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks is introduced, which is used to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse.
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
It is shown in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models the authors learn, and Classical supervised machine learning approaches can be applied in order to learn prediction models.
Towards time-aware link prediction in evolving social networks
The results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently.
Supervised Rank Aggregation for Predicting Influencers in Twitter
  • Karthik Subbian, Prem Melville
  • Computer Science
    2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing
  • 2011
This work proposes an approach to supervised rank aggregation, driven by techniques from Social Choice Theory, and illustrates the effectiveness of this method through experiments on a data set of 40 million Twitter users.
The link-prediction problem for social networks
Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.