Books
| Practical Graph Analytics with Apache Giraph (Published by Apress) Practical Graph Analytics with Apache Giraph helps you build data mining and machine learning applications using the Apache Foundation’s Giraph framework for graph processing. This is the same framework as used by Facebook, Google, and other social media analytics operations to derive business value from vast amounts of interconnected data points. |
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Research Articles
Sergey Edunov, Dionysios Logothetis, Avery Ching, Maja Kabiljo, Cheng Wang, Darwini: Generating realistic large-scale social graphs.
IEEE International Conference on Distributed Computing Systems, 2018
https://easychair.org/publications/preprint/5vJS
Martella, Claudio et al. Spinner: scalable graph partitioning for the cloud.
IEEE International Conference on Data Engineering, 2017
https://ieeexplore.ieee.org/document/7930049/
Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, Sambavi Muthukrishnan, One Trillion Edges: Graph Processing at Facebook-Scale.
Proceedings of the VLDB Endowment, Vol. 8, No. 12, (2015)
http://www.vldb.org/pvldb/vol8/p1804-ching.pdf
Xin, R. S., Crankshaw, D., Dave, A., Gonzalez, J. E., Franklin, M. J., & Stoica, I. GraphX: Unifying Data-Parallel and Graph-Parallel Analytics.
arXiv preprint arXiv:1402.2394. (2014)
http://arxiv.org/pdf/1402.2394
Khayyat, Zuhair, et al. Mizan: a system for dynamic load balancing in large-scale graph processing.
Proceedings of the 8th ACM European Conference on Computer Systems. ACM, (2013).
http://www.cs.cornell.edu/~djwill/pubs/mizan.pdf
Salihoglu, Semih, & Jennifer Widom. Gps: A graph processing system.
Proceedings of the 25th International Conference on Scientific and Statistical Database Management. ACM, (2013).
http://ilpubs.stanford.edu:8090/1039/7/gps_ssdbm.pdf
Tian, Y., Balmin, A., Corsten, S. A., Tatikonda, S., & McPherson, J. From Think Like a Vertex to Think Like a Graph.
Proceedings of the VLDB Endowment, 7(3). (2013)
http://researcher.ibm.com/researcher/files/us-ytian/giraph++.pdf
Schelter, S., Ewen, S., Tzoumas, K., & Markl, V. All roads lead to Rome: optimistic recovery for distributed iterative data processing.
In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 1919-1928). ACM. (2013, October).
http://stratosphere.eu/assets/papers/optimistic.pdf
Ewen, S., Tzoumas, K., Kaufmann, M., & Markl, V. Spinning fast iterative data flows. Proceedings of the VLDB Endowment, 5(11), 1268-1279. (2012).
http://arxiv.org/pdf/1208.0088.pdf?origin=publication_detail
Malewicz, Grzegorz, et al. Pregel: a system for large-scale graph processing. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, (2010).
http://static.cs.brown.edu/courses/csci2270/papers/pregel.pdf
Valiant, Leslie G. A bridging model for parallel computation. Communications of the ACM 33.8 : 103-111. (1990).
http://web.mit.edu/6.976/www/handout/valiant2.pdf
Hong, Sungpack, et al. Green-Marl: a DSL for easy and efficient graph analysis. ACM SIGARCH Computer Architecture News. Vol. 40. No. 1. ACM, (2012).
http://www.cl.cam.ac.uk/~ey204/teaching/ACS/R202_2012_2013/papers/S7_Network_Structure/papers/hong_asplos_2012.pdf
Salihoglu, Semih, and Jennifer Widom. Optimizing Graph Algorithms on Pregel-like Systems. (2014).
http://ilpubs.stanford.edu:8090/1077/3/p535-salihoglu.pdf
Salihoglu, Semih, and Jennifer Widom. HelP: High-level Primitives For Large-Scale Graph Processing.
http://ilpubs.stanford.edu:8090/1085/2/primitives_tr_sig_alternate.pdf