Summarizing static and dynamic big graphs
Large-scale, highly-interconnected networks pervade our society and the natural world around us, including the World Wide Web, social networks, knowledge graphs, genome and scientific databases, medical and government records. The massive scale of graph data often surpasses the available computation...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/105715 http://hdl.handle.net/10220/49546 |
| _version_ | 1826114898413748224 |
|---|---|
| author | Khan, Arijit Bhowmick, Sourav Saha Bonchi, Francesco |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Khan, Arijit Bhowmick, Sourav Saha Bonchi, Francesco |
| author_sort | Khan, Arijit |
| collection | NTU |
| description | Large-scale, highly-interconnected networks pervade our society and the natural world around us, including the World Wide Web, social networks, knowledge graphs, genome and scientific databases, medical and government records. The massive scale of graph data often surpasses the available computation and storage resources. Besides, users get overwhelmed by the daunting task of understanding and using such graphs due to their sheer volume and complexity. Hence, there is a critical need to summarize large graphs into concise forms that can be more easily visualized, processed, and managed. Graph summarization has indeed attracted a lot of interests from various research communities, such as sociology, physics, chemistry, bioinformatics, and computer science. Different ways of summarizing graphs have been invented that are often complementary to each other. In this tutorial, we discuss algorithmic advances on graph summarization in the context of both classical (e.g., static graphs) and emerging (e.g., dynamic and stream graphs) applications. We emphasize the current challenges and highlight some future research directions. |
| first_indexed | 2024-10-01T03:46:43Z |
| format | Journal Article |
| id | ntu-10356/105715 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:46:43Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/1057152020-09-14T01:53:08Z Summarizing static and dynamic big graphs Khan, Arijit Bhowmick, Sourav Saha Bonchi, Francesco School of Computer Science and Engineering Engineering::Computer science and engineering Query Processing Visualization Large-scale, highly-interconnected networks pervade our society and the natural world around us, including the World Wide Web, social networks, knowledge graphs, genome and scientific databases, medical and government records. The massive scale of graph data often surpasses the available computation and storage resources. Besides, users get overwhelmed by the daunting task of understanding and using such graphs due to their sheer volume and complexity. Hence, there is a critical need to summarize large graphs into concise forms that can be more easily visualized, processed, and managed. Graph summarization has indeed attracted a lot of interests from various research communities, such as sociology, physics, chemistry, bioinformatics, and computer science. Different ways of summarizing graphs have been invented that are often complementary to each other. In this tutorial, we discuss algorithmic advances on graph summarization in the context of both classical (e.g., static graphs) and emerging (e.g., dynamic and stream graphs) applications. We emphasize the current challenges and highlight some future research directions. MOE (Min. of Education, S’pore) Published version 2019-08-06T02:43:37Z 2019-12-06T21:56:26Z 2019-08-06T02:43:37Z 2019-12-06T21:56:26Z 2017 Journal Article Khan, A., Bhowmick, S. S., & Bonchi, F. (2017). Summarizing static and dynamic big graphs. Proceedings of the VLDB Endowment, 10(12), 1981-1984. doi:10.14778/3137765.3137825 2150-8097 https://hdl.handle.net/10356/105715 http://hdl.handle.net/10220/49546 10.14778/3137765.3137825 en Proceedings of the VLDB Endowment © 2017 VLDB Endowment. This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. 4 p. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Query Processing Visualization Khan, Arijit Bhowmick, Sourav Saha Bonchi, Francesco Summarizing static and dynamic big graphs |
| title | Summarizing static and dynamic big graphs |
| title_full | Summarizing static and dynamic big graphs |
| title_fullStr | Summarizing static and dynamic big graphs |
| title_full_unstemmed | Summarizing static and dynamic big graphs |
| title_short | Summarizing static and dynamic big graphs |
| title_sort | summarizing static and dynamic big graphs |
| topic | Engineering::Computer science and engineering Query Processing Visualization |
| url | https://hdl.handle.net/10356/105715 http://hdl.handle.net/10220/49546 |
| work_keys_str_mv | AT khanarijit summarizingstaticanddynamicbiggraphs AT bhowmicksouravsaha summarizingstaticanddynamicbiggraphs AT bonchifrancesco summarizingstaticanddynamicbiggraphs |