Fetching the paper…
Reading the bibliography…
This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection).
Finding all cliques of an undirected graph (algorithm 457)
C. Bron and J. Kerbosch · 1973
Earlier work this paper cites.
An efficient branch-and-bound algorithm for finding a maximum clique
E. Tomita and T. Seki · 2003
Earlier work this paper cites.
Mapreduce: Simplified data processing on large clusters
J. Dean and S. Ghemawat · 2004
Earlier work this paper cites.
Graphs-at-a-time: query language and access methods for graph databases
H. He and A. K. Singh · 2008
Earlier work this paper cites.
NAGA: searching and ranking knowledge
G. Kasneci, F. M. Suchanek, G. Ifrim, M. Ramanath, and G. Weikum · 2008
Earlier work this paper cites.
Effective pruning techniques for mining quasi-cliques
G. Liu and L. Wong · 2008
Earlier work this paper cites.
On finding dense subgraphs
S. Khuller and B. Saha · 2009
Earlier work this paper cites.
Distancejoin: Pattern match query in a large graph database
L. Zou, L. Chen, and M. T. Özsu · 2009
Earlier work this paper cites.
Pregel: a system for large-scale graph processing
G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski · 2010
Earlier work this paper cites.
Powergraph: Distributed graph-parallel computation on natural graphs
J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin · 2012
Earlier work this paper cites.
An in-depth comparison of subgraph isomorphism algorithms in graph databases
J. Lee, W. Han, R. Kasperovics, and J. Lee · 2012
Earlier work this paper cites.
Distributed GraphLab: A framework for machine learning in the cloud
Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein · 2012
Cited alongside, same era.
Using pregel-like large scale graph processing frameworks for social network analysis
L. Quick, P. Wilkinson, and D. Hardcastle · 2012
Cited alongside, same era.
Mining of massive datasets
A. Rajaraman, J. D. Ullman, J. D. Ullman, and J. D. Ullman · 2012
Cited alongside, same era.
Probase: a probabilistic taxonomy for text understanding
W. Wu, H. Li, H. Wang, and K. Q. Zhu · 2012
Cited alongside, same era.
On clique relaxation models in network analysis
J. Pattillo, N. Youssef, and S. Butenko · 2013
Cited alongside, same era.
GPS: a graph processing system
S. Salihoglu and J. Widom · 2013
Cited alongside, same era.
I/o-efficient algorithms on triangle listing and counting
X. Hu, Y. Tao, and C. Chung · 2014
Later among the works it cites.
Eagr: supporting continuous ego-centric aggregate queries over large dynamic graphs
J. Mondal and A. Deshpande · 2014
Later among the works it cites.
Scalable big graph processing in mapreduce
L. Qin, J. X. Yu, L. Chang, H. Cheng, C. Zhang, and X. Lin · 2014
Later among the works it cites.
Nscale: neighborhood-centric large-scale graph analytics in the cloud
A. Quamar, A. Deshpande, and J. Lin · 2014
Later among the works it cites.
Parallel subgraph listing in a large-scale graph
Y. Shao, B. Cui, L. Chen, L. Ma, J. Yao, and N. Xu · 2014
Later among the works it cites.
Blogel: A block-centric framework for distributed computation on real-world graphs
D. Yan, J. Cheng, Y. Lu, and W. Ng · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
From ”think like a vertex” to “think like a graph”
Y. Tian, A. Balmin, S. A. Corsten, S. Tatikonda, and J. McPherson · 2013
Cited alongside, same era.
Scalable maximum clique computation using mapreduce
J. Xiang, C. Guo, and A. Aboulnaga · 2013
Cited alongside, same era.
Pregelix: Big(ger) graph analytics on a dataflow engine
Y. Bu, V. R. Borkar, J. Jia, M. J. Carey, and T. Condie · 2014
Cited alongside, same era.
Continuous pattern detection over billion-edge graph using distributed framework
J. Gao, C. Zhou, J. Zhou, and J. X. Yu · 2014
Cited alongside, same era.
Graphx: Graph processing in a distributed dataflow framework
J. E. Gonzalez, R. S. Xin, A. Dave, D. Crankshaw, M. J. Franklin, and I. Stoica · 2014
Cited alongside, same era.
Pregel algorithms for graph connectivity problems with performance guarantees
D. Yan, J. Cheng, K. Xing, Y. Lu, W. Ng, and Y. Bu · 2014
Later among the works it cites.
One trillion edges: Graph processing at facebook-scale
A. Ching, S. Edunov, M. Kabiljo, D. Logothetis, and S. Muthukrishnan · 2015
Later among the works it cites.
Arabesque: a system for distributed graph mining
C. H. C. Teixeira, A. J. Fonseca, M. Serafini, G. Siganos, M. J. Zaki, and A. Aboulnaga · 2015
Later among the works it cites.
Effective techniques for message reduction and load balancing in distributed graph computation
D. Yan, J. Cheng, Y. Lu, and W. Ng · 2015
Later among the works it cites.
Big graph analytics platforms
D. Yan, Y. Bu, Y. Tian, and A. Deshpande · 2017
Closest in time.