Szczegóły publikacji
Opis bibliograficzny
ivga: visualization of the network of historical events / Witold DZWINEL, Rafał WCISŁO, Magdalena Strzoda // W: IML 2017 [Dokument elektroniczny] : international conference on Internet of things and Machine Learning : October 17–18, 2017, Liverpool, United Kingdom. — New York : Association for Computing Machinery (ACM) International Conference Proceedings Series (ICPS), cop. 2017. — e-ISBN: 978-1-4503-5243-7. — S. [1–7] art. no. 38. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [7], Abstr.
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 114226 |
|---|---|
| Data dodania do BaDAP | 2018-06-08 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3109761.3158412 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | International conference on Internet of things and Machine Learning |
Abstract
There are many tools for the analysis of social networks such as the algorithms for community detection. However, visualization of these networks enables not only to recognize their important structural features and forms, such as clusters of vertices and their connectivity patterns, but also to assess their mutual relationships in terms of position, distance, shape and connection density. As we have demonstrated in our recent paper, a new method for interactive visualization of graphs (ivga) allows for instant visualization of large social networks, consisting of a few million of vertices and scores of million edges. Here, we estimate its visualization precision by investigating the network of historical events generated from English Wikipedia. The Wikipedia articles about historical events are the vertices of this graph while the hyperlinks to other articles represent its edges. We show that the network reveals a very distinct multi-scale structural properties. Its visual and interactive exploration allows for better understanding the casual relationships between both single historical events and their clusters.