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Biased Dynamic Sampling for Temporal Network Streams

  • Shazia Tabassum
  • João Gama
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Considering the avalanche of evolving data and the memory constraints, streaming networks’ sampling has gained much attention in the recent decade. However, samples choosing data uniformly from the beginning to the end of a temporal stream are not very relevant for temporally evolving networks where recent activities are more important than the old events. Moreover, the relationships also change overtime. Recent interactions are evident to show the current status of relationships, nevertheless some old stronger relations are also substantially significant. Considering the above issues we propose a fast memory less dynamic sampling mechanism for weighted or multi-graph high-speed streams. For this purpose, we use a forgetting function with two parameters that help introduce biases on the network based on time and relationship strengths. Our experiments on real-world data sets show that our samples not only preserve the basic properties like degree distributions but also maintain the temporal distribution correlations. We also observe that our method generates samples with increased efficiency. It also outperforms current sampling algorithms in the area.

Notes

Acknowledgement

This research was carried out in the framework of the project TEC4Growth – RL SMILES – Smart, mobile, Intelligent and Large Scale Sensing and analytics NORTE-01-0145-FEDER-000020 which is financed by the north Portugal regional operational program NORTE 2020 and partly funded by PRODEI program, Faculdade de Engenharia, Universidade do Porto.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INESC TECUniversity of PortoPortoPortugal
  2. 2.Faculdade de EngenhariaUniversity of PortoPortoPortugal
  3. 3.Faculdade de EconomiaUniversity of PortoPortoPortugal

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