Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Abstract
Datasets to study the temporal evolution of graphs are scarce. To encourage the research of novel dynamic graph learning algorithms we introduce YoutubeGraph-Dyn (available at https://github.com/palash1992/YoutubeGraph-Dyn), an evolving graph dataset generated from YouTube real-world interactions. YoutubeGraph-Dyn provides intra-day time granularity (with 416 snapshots taken every 6 hours for a period of 104 days), multi-modal relationships that capture different aspects of the data, multiple attributes including timestamped, non-timestamped, word embeddings, and integers. Our data collection methodology emphasizes the creation of time evolving graphs from non-timestamped data. In this paper, we provide various graph statistics of YoutubeGraph-Dyn and test state-of-the-art graph clustering algorithms to detect community migration, and time series analysis and recurrent neural network algorithms to forecast non-timestamped data.
Keywords
Cite
@article{arxiv.1907.02222,
title = {Tracking Temporal Evolution of Graphs using Non-Timestamped Data},
author = {Sujit Rokka Chhetri and Palash Goyal and Arquimedes Canedo},
journal= {arXiv preprint arXiv:1907.02222},
year = {2019}
}