Within-Network Classification in Temporal Graphs
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Proceedings, 2018 IEEE International Conference on Data Mining Workshops (ICDMW) |
Publisher | IEEE |
Publication date | 2018 |
Pages | 229-236 |
DOIs | |
Publication status | Published - 2018 |
Event | 2018 IEEE International Conference on Data Mining Workshops (ICDMW) - Singapore, Singapore Duration: 17 Nov 2018 → 20 Nov 2018 |
Conference
Conference | 2018 IEEE International Conference on Data Mining Workshops (ICDMW) |
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Location | Singapore, Singapore |
Periode | 17/11/2018 → 20/11/2018 |
ID: 239567139