Distributed Graph Processing: Techniques and Systems
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1373 CCIS, Page: 14-23
2021
- 5Captures
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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
- Captures5
- Readers5
Conference Paper Description
During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallel processing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85107351219&origin=inward; http://dx.doi.org/10.1007/978-981-16-0479-9_2; https://link.springer.com/10.1007/978-981-16-0479-9_2; https://link.springer.com/content/pdf/10.1007/978-981-16-0479-9_2; https://dx.doi.org/10.1007/978-981-16-0479-9_2; https://link.springer.com/chapter/10.1007/978-981-16-0479-9_2
Springer Science and Business Media LLC
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