Scalable Temporal Motif Densest Subnetwork Discovery
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ISSN: 2154-817X, Page: 2536-2547
2024
- 1Citations
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- CrossRef1
- Captures2
- Readers2
Conference Paper Description
Finding dense subnetworks, with density based on edges or more complex structures, such as subgraphs or k-cliques, is a fundamental algorithmic problem with many applications. While the problem has been studied extensively in static networks, much remains to be explored for temporal networks. In this work we introduce the novel problem of identifying the temporal motif densest subnetwork, i.e., the densest subnetwork with respect to temporal motifs, which are high-order patterns characterizing temporal networks. Identifying temporal motifs is an extremely challenging task, and thus, efficient methods are required. To address this challenge, we design two novel randomized approximation algorithms with rigorous probabilistic guarantees that provide high-quality solutions. We perform extensive experiments showing that our methods outperform baselines. Furthermore, our algorithms scale on networks with up to billions of temporal edges, while baselines cannot handle such large networks. We use our techniques to analyze a financial network and show that our formulation reveals important network structures, such as bursty temporal events and communities of users with similar interests.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know