Within-Network Classification in Temporal Graphs

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Within-Network Classification in Temporal Graphs. / Ryther, Christopher; Simonsen, Jakob.

Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. p. 229-236.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Ryther, C & Simonsen, J 2018, Within-Network Classification in Temporal Graphs. in Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp. 229-236, 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 17/11/2018. https://doi.org/10.1109/ICDMW.2018.00041

APA

Ryther, C., & Simonsen, J. (2018). Within-Network Classification in Temporal Graphs. In Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 229-236). IEEE. https://doi.org/10.1109/ICDMW.2018.00041

Vancouver

Ryther C, Simonsen J. Within-Network Classification in Temporal Graphs. In Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE. 2018. p. 229-236 https://doi.org/10.1109/ICDMW.2018.00041

Author

Ryther, Christopher ; Simonsen, Jakob. / Within-Network Classification in Temporal Graphs. Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. pp. 229-236

Bibtex

@inproceedings{8edfda5175164765b2ffdb9402374397,
title = "Within-Network Classification in Temporal Graphs",
abstract = "Recent results indicate that static graph features might not be adequate to solve challenges in graphs involving a temporal dimension. We analyze several classification problems using already established temporal metrics, and we propose label-sensitive and recency-sensitive variants of these metrics that capture labeling information and additional temporal patterns in the data. We test all new and old metrics, and a baseline based on a standard disease-spreading model, using tuned off-the-shelf classifiers on 9 datasets of varying size and usage domain. Our experiments indicate that usage of label-and recency-sensitive metrics on real-world data provides more accurate results than static approaches and approaches based on temporal metrics alone.",
author = "Christopher Ryther and Jakob Simonsen",
year = "2018",
doi = "10.1109/ICDMW.2018.00041",
language = "English",
pages = "229--236",
booktitle = "Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW)",
publisher = "IEEE",
note = "2018 IEEE International Conference on Data Mining Workshops (ICDMW) ; Conference date: 17-11-2018 Through 20-11-2018",

}

RIS

TY - GEN

T1 - Within-Network Classification in Temporal Graphs

AU - Ryther, Christopher

AU - Simonsen, Jakob

PY - 2018

Y1 - 2018

N2 - Recent results indicate that static graph features might not be adequate to solve challenges in graphs involving a temporal dimension. We analyze several classification problems using already established temporal metrics, and we propose label-sensitive and recency-sensitive variants of these metrics that capture labeling information and additional temporal patterns in the data. We test all new and old metrics, and a baseline based on a standard disease-spreading model, using tuned off-the-shelf classifiers on 9 datasets of varying size and usage domain. Our experiments indicate that usage of label-and recency-sensitive metrics on real-world data provides more accurate results than static approaches and approaches based on temporal metrics alone.

AB - Recent results indicate that static graph features might not be adequate to solve challenges in graphs involving a temporal dimension. We analyze several classification problems using already established temporal metrics, and we propose label-sensitive and recency-sensitive variants of these metrics that capture labeling information and additional temporal patterns in the data. We test all new and old metrics, and a baseline based on a standard disease-spreading model, using tuned off-the-shelf classifiers on 9 datasets of varying size and usage domain. Our experiments indicate that usage of label-and recency-sensitive metrics on real-world data provides more accurate results than static approaches and approaches based on temporal metrics alone.

U2 - 10.1109/ICDMW.2018.00041

DO - 10.1109/ICDMW.2018.00041

M3 - Article in proceedings

SP - 229

EP - 236

BT - Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW)

PB - IEEE

T2 - 2018 IEEE International Conference on Data Mining Workshops (ICDMW)

Y2 - 17 November 2018 through 20 November 2018

ER -

ID: 239567139