Biased Dynamic Sampling for Temporal Network Streams

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Tabassum, S (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2018, Cambridge, UK, December 11-13, 2018.

Pages: 512-523

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00Q-1F4

DOI: 10.1007/978-3-030-05411-3_42

Abstract (EN): 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. © 2019, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific