Tracking Temporal Evolution of Graphs using Non-Timestamped Data

July 04, 2019 · Entered Twilight · 🏛 ACM Conference on Hypertext & Social Media

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Repo contents: README.md, anonymized_graphs, anonymized_timeseries

Authors Sujit Rokka Chhetri, Palash Goyal, Arquimedes Canedo arXiv ID 1907.02222 Category cs.SI: Social & Info Networks Citations 1 Venue ACM Conference on Hypertext & Social Media Repository https://github.com/palash1992/YoutubeGraph-Dyn ⭐ 27 Last Checked 2 months ago

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.