Name
Abstract | ||
|---|---|---|
Time-evolving graphs are structures that encapsulate how a graph changes over time. Thus, we not only have to deal with large graphs consisting of nodes and edges in the billions, but we must also keep track of when these edges activate and deactivate over long lifetimes. In this age of big historical data, we must make use of efficient time-evolving graph compressions, or we will find ourselves quickly out of main memory. These time-evolving graph compressions must not only be space efficient, but must also facilitate fast querying directly on the compressed graph. In this paper, define several novel time-evolving graph problems and develop algorithms to solve them directly on various, massive, synthetic and real-world time-evolving graphs compressed using our technique. Our experiments provide details of the compressed graph sizes, algorithm run times, and other metrics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9005704 | 2019 IEEE International Conference on Big Data (Big Data) |
Keywords | Field | DocType |
compressed time-evolving graphs,big historical data,efficient time-evolving graph compressions,time-evolving graph problems,real-world time-evolving graphs,compressed graph sizes | Graph,Computer science,Algorithm | Conference |
ISSN | ISBN | Citations |
2639-1589 | 978-1-7281-0859-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael L. Nelson | 1 | 6 | 1.47 |
| Sridhar Radhakrishnan | 2 | 341 | 43.65 |
| Chandra N. Sekharan | 3 | 105 | 12.77 |