Graphs have attracted research efforts for about three centuries (Biggs et al. 1986). Earlier, the focus was mainly on mathematical models and algorithms. Graph theory continues to be an active research area that includes many open problems. In the last decade, there has been a considerable amount of research in analyzing and processing large graph structures for real applications. This effort has led to the development of several alternative algorithms, techniques, and big data systems.
Parallel systems are very important and big data challenges are relevant to graph analytics. Although the edge list of some existing graphs are relatively small in size and may fit in one machine, these graphs are often large when considering vertex properties, graph indexes, and intermediate results during query processing. Processing often leads to very large memory requirements, which makes it not practical to fit the entire graph and its data into a single machine.
Big graph data has a...
- Anderson MJ, Sundaram N, Satish N, Patwary MMA, Willke TL, Dubey P (2016) Graphpad: optimized graph primitives for parallel and distributed platforms. In: Proceedings of the 30th international parallel and distributed processing symposium, pp 313–322Google Scholar
- Bader DA, Madduri K (2005) Design and implementation of the hpcs graph analysis benchmark on symmetric multiprocessors. In: International conference on high-performance computing, pp 465–476Google Scholar
- Beamer S, Asanović K, Patterson D (2015) The gap benchmark suite. arXiv preprint arXiv:150803619Google Scholar
- Biggs N, Lloyd EK, Wilson RJ (1986) Graph theory. Clarendon Press, New York, pp 1736–1936Google Scholar
- Boldi P, Vigna S (2004) The webgraph framework I: compression techniques, pp 595–601Google Scholar
- Boldi P, Rosa M, Santini M, Vigna S (2011) Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks, In: Proceedings of the 20th international conference on world wide web, pp 587–596Google Scholar
- Chakrabarti D, Faloutsos C, McGlohon M (2010) Graph mining: laws and generators. In: Aggarwal CC (ed) Managing and mining graph data. Springer, pp 69–123Google Scholar
- Ciglan M, Averbuch A, Hluchy L (2012) Benchmarking traversal operations over graph databases. In: Proceedings of the workshops of 28th international conference on data engineering. IEEE, pp 186–189Google Scholar
- Erling O, Averbuch A, Larriba-Pey J, Chafi H, Gubichev A, Prat A, Pham MD, Boncz P (2015) The LDBC social network benchmark: interactive workload, In: Proceedings of the 2015 ACM SIGMOD international conference on management of data. ACM, pp 619–630Google Scholar
- Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. In: ACM SIGCOMM computer communication review, vol 29. ACM, pp 251–262Google Scholar
- Hong S, Depner S, Manhardt T, Lugt JVD, Verstraaten M, Chafi H (2015) Pgx.d: a fast distributed graph processing engine. In: Proceedings of international conference for high performance computing, networking, storage and analysis, pp 1–12Google Scholar
- Leskovec J, Krevl A (2014) SNAP Datasets: stanford large network dataset collection. http://snap.stanford.edu/data
- Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C (2005a) Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In: Proceedings of the 9th European conference on principles of data mining and knowledge discovery, vol 5, pp 133–145CrossRefGoogle Scholar
- Leskovec J, Kleinberg J, Faloutsos C (2005b) Graphs over time: Densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining, pp 177–187Google Scholar
- Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In: Proceedings of ACM SIGMOD international conference on management of data, pp 135–146Google Scholar
- McSherry F, Isard M, Murray DG (2015) Scalability! but at what cost? In: Proceedings of the 15th USENIX conference on hot topics in operating systemsGoogle Scholar
- Murphy RC, Wheeler KB, Barrett BW, Ang JA (2010) Introducing the graph 500. Cray Users Group (CUG)Google Scholar
- Wang L, Zhan J, Luo C, Zhu Y, Yang Q, He Y, Gao W, Jia Z, Shi Y, Zhang S, Zheng C, Lu G, Zhan K, Li X, Qiu B (2014) Bigdatabench: a big data benchmark suite from internet services. In: International symposium on high performance computer architecture, pp 488–499Google Scholar