Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3342220.3343664 | Proceedings of the 30th ACM Conference on Hypertext and Social Media |
Keywords | Field | DocType |
dataset, dynamic graph embedding, machine learning, social network analysis | Graph,Information retrieval,Computer science,Multimedia | Conference |
ISBN | Citations | PageRank |
978-1-4503-6885-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujit Rokka Chhetri | 1 | 50 | 5.76 |
| Palash Goyal | 2 | 0 | 0.68 |
| Arquimedes Canedo | 3 | 143 | 23.31 |