44,595 research outputs found
Predicting links in ego-networks using temporal information
Link prediction appears as a central problem of network science, as it calls
for unfolding the mechanisms that govern the micro-dynamics of the network. In
this work, we are interested in ego-networks, that is the mere information of
interactions of a node to its neighbors, in the context of social
relationships. As the structural information is very poor, we rely on another
source of information to predict links among egos' neighbors: the timing of
interactions. We define several features to capture different kinds of temporal
information and apply machine learning methods to combine these various
features and improve the quality of the prediction. We demonstrate the
efficiency of this temporal approach on a cellphone interaction dataset,
pointing out features which prove themselves to perform well in this context,
in particular the temporal profile of interactions and elapsed time between
contacts.Comment: submitted to EPJ Data Scienc
RankMerging: A supervised learning-to-rank framework to predict links in large social network
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 substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
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