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On fast and scalable recurring link’s prediction in evolving multi-graph streams

Published online by Cambridge University Press:  20 January 2020

Shazia Tabassum Open the ORCID record for Shazia Tabassum [Opens in a new window] ,
Bruno Veloso  and
João Gama
Shazia Tabassum*
Affiliation:
INESC TEC and FEUP, University of Porto, Porto, Portugal (e-mail: [email protected])
Bruno Veloso
Affiliation:
INESC TEC, University Portucalense, Porto, Portugal (e-mail: [email protected])
João Gama
Affiliation:
INESC TEC and FEP, University of Porto, Porto, Portugal (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

Keywords

Hocine Cherilink predictionevolving networkrecurring linksgraph streamstemporal networks
Type
Research Article
Information
Network Science , Volume 8 , Issue S1: Complex Networks 2018 , July 2020 , pp. S65 - S81
Copyright
© Cambridge University Press 2020

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