Similarity Based Compression Ratio for Dynamic Network Modelling

Creative Commons License

Orman G. K., Colak S.

2021 IEEE 19th International Conference on Smart Technologies (EUROCON), Lviv, Ukrayna, 6 - 08 Temmuz 2021 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/eurocon52738.2021.9535635
  • Basıldığı Şehir: Lviv
  • Basıldığı Ülke: Ukrayna
  • Anahtar Kelimeler: dynamic network, complex systems, network similarity, compression ratio
  • Galatasaray Üniversitesi Adresli: Evet

Özet

Dynamic network modelling of timely evolving complex systems allows to discover emerging properties of real-world facts. The main issue of such modelling is determining the proper time intervals, a.k.a. window size, for each member of network. In this work, we propose a new network similarity based compression ratio for measuring the properness of studied window size. Besides, we show that a more informative dynamic network with a less noisier structure can be extracted by using a window aggregation strategy. The results on Enron, Haggle Infocom and Reality Mining data sets reveal that the proposed compression ratio is more effective for finding best window size than baseline and aggregation strategy allows to capture important time-dependent events which might be hidden in noise when using constant windows.