Fetching the paper…
Reading the bibliography…
Link prediction is one of the central problems in graph mining.
A new status index derived from sociometric analysis
L. Katz · 1953
Earlier work this paper cites.
Algorithm 457: finding all cliques of an undirected graph
C. Bron and J. Kerbosch · 1973
Earlier work this paper cites.
Collective dynamics of ‘small-world’networks
D. J. Watts and S. H. Strogatz · 1998
Earlier work this paper cites.
Comparative assessment of large-scale data sets of protein–protein interactions
C. Von Mering et al · 2002
Earlier work this paper cites.
Friends and neighbors on the web
L. A. Adamic and E. Adar · 2003
Earlier work this paper cites.
State of the art of graph-based data mining
T. Washio and H. Motoda · 2003
Earlier work this paper cites.
Cyclic pattern kernels for predictive graph mining
T. Horváth, T. Gärtner, and S. Wrobel · 2004
Earlier work this paper cites.
Link prediction in relational data
B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller · 2004
Earlier work this paper cites.
Discovering large dense subgraphs in massive graphs
D. Gibson, R. Kumar, and A. Tomkins · 2005
Earlier work this paper cites.
Interaction graph mining for protein complexes using local clique merging
X.-L. Li, C.-S. Foo, S.-H. Tan, and S.-K. Ng · 2005
Earlier work this paper cites.
Link prediction using supervised learning
M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki · 2006
Earlier work this paper cites.
Pajek datasets, 2006
V. Batagelj and A. Mrvar · 2006
Earlier work this paper cites.
Graph mining: Laws, generators, and algorithms
D. Chakrabarti and C. Faloutsos · 2006
Earlier work this paper cites.
Mining graph data
D. J. Cook and L. B. Holder · 2006
Earlier work this paper cites.
Matching structure and semantics: A survey on graph-based pattern matching
B. Gallagher · 2006
Earlier work this paper cites.
Vertex similarity in networks
E. A. Leicht, P. Holme, and M. E. Newman · 2006
Earlier work this paper cites.
The worst-case time complexity for generating all maximal cliques and computational experiments
E. Tomita, A. Tanaka, and H. Takahashi · 2006
Earlier work this paper cites.
The link-prediction problem for social networks
D. Liben-Nowell and J. Kleinberg · 2007
Earlier work this paper cites.
A note on the problem of reporting maximal cliques
F. Cazals and C. Karande · 2008
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2008
Earlier work this paper cites.
Mining the largest quasi-clique in human protein interactome
M. Bhattacharyya and S. Bandyopadhyay · 2009
Earlier work this paper cites.
A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets
K. Madduri, D. Ediger, K. Jiang, D. A. Bader, and D. Chavarria-Miranda · 2009
Earlier work this paper cites.
Managing and mining graph data
C. C. Aggarwal and H. Wang · 2010
Earlier work this paper cites.
Massive streaming data analytics: A case study with clustering coefficients
D. Ediger et al · 2010
Earlier work this paper cites.
Listing all maximal cliques in sparse graphs in near-optimal time
D. Eppstein, M. Löffler, and D. Strash · 2010
Earlier work this paper cites.
A survey of algorithms for dense subgraph discovery
V. E. Lee, N. Ruan, R. Jin, and C. Aggarwal · 2010
Earlier work this paper cites.
Graph mining applications to social network analysis
L. Tang and H. Liu · 2010
Earlier work this paper cites.
A survey of link prediction in social networks
M. Al Hasan and M. J. Zaki · 2011
Earlier work this paper cites.
The combinatorial blas: Design, implementation, and applications
A. Buluç and J. R. Gilbert · 2011
Earlier work this paper cites.
Temporal motifs in time-dependent networks
L. Kovanen et al · 2011
Earlier work this paper cites.
Link prediction in complex networks: A survey
L. Lü and T. Zhou · 2011
Earlier work this paper cites.
Stinger: High performance data structure for streaming graphs
D. Ediger, R. McColl, J. Riedy, and D. A. Bader · 2012
Earlier work this paper cites.
Temporal motifs reveal the dynamics of editor interactions in wikipedia
D. Jurgens and T.-C. Lu · 2012
Earlier work this paper cites.
Graph mining: A survey of graph mining techniques
S. U. Rehman, A. U. Khan, and S. Fong · 2012
Earlier work this paper cites.
A survey of frequent subgraph mining algorithms
C. Jiang, F. Coenen, and M. Zito · 2013
Cited alongside, same era.
Temporal motifs
L. Kovanen, M. Karsai, K. Kaski, J. Kertész, and J. Saramäki · 2013
Cited alongside, same era.
A new parallel algorithm for connected components in dynamic graphs
R. McColl, O. Green, and D. A. Bader · 2013
Cited alongside, same era.
Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
Cited alongside, same era.
Accelerating Irregular Computations with Hardware Transactional Memory and Active Messages
M. Besta and T. Hoefler · 2015
Cited alongside, same era.
Frequent subgraph mining algorithms-a survey
T. Ramraj and R. Prabhakar · 2015
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
Later among the works it cites.
Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
Later among the works it cites.
Cloud programming simplified: A berkeley view on serverless computing
E. Jonas, J. Schleier-Smith, V. Sreekanti, C.-C. Tsai, A. Khandelwal, Q. Pu, V. Shankar, J. Carreira, K. Krauth, N. Yadwadkar, et al · 2019
Later among the works it cites.
Sampling methods for counting temporal motifs
P. Liu, A. R. Benson, and M. Charikar · 2019
Later among the works it cites.
Pairwise link prediction
H. Nassar, A. R. Benson, and D. F. Gleich · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Higher-order organization of complex networks
A. R. Benson, D. F. Gleich, and J. Leskovec · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
Cited alongside, same era.
Mathematical foundations of the graphblas
J. Kepner et al · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
Cited alongside, same era.
A survey of link prediction in complex networks
V. Martínez, F. Berzal, and J.-C. Cubero · 2016
Cited alongside, same era.
Serverless computing: Current trends and open problems
I. Baldini, P. Castro, K. Chang, P. Cheng, S. Fink, V. Ishakian, N. Mitchell, V. Muthusamy, R. Rabbah, A. Slominski, et al · 2017
Cited alongside, same era.
P. Ribeiro, P. Paredes, M. E. Silva, D. Aparicio, and F. Silva · 2019
Later among the works it cites.
Heterogeneous graph neural network
C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla · 2019
Later among the works it cites.
Graph convolutional networks: a comprehensive review
S. Zhang, H. Tong, J. Xu, and R. Maciejewski · 2019
Later among the works it cites.
Aligraph: A comprehensive graph neural network platform
R. Zhu, K. Zhao, H. Yang, W. Lin, C. Zhou, B. Ai, Y. Li, and J. Zhou · 2019
Later among the works it cites.
Communication-efficient jaccard similarity for high-performance distributed genome comparisons
M. Besta et al · 2020
Later among the works it cites.
Substream-centric maximum matchings on fpga
M. Besta, M. Fischer, T. Ben-Nun, D. Stanojevic, J. D. F. Licht, and T. Hoefler · 2020
Later among the works it cites.
High-performance parallel graph coloring with strong guarantees on work, depth, and quality
M. Besta and otherd · 2020
Later among the works it cites.
A comprehensive survey on geometric deep learning
W. Cao, Z. Yan, Z. He, and Z. He · 2020
Later among the works it cites.
Machine learning on graphs: A model and comprehensive taxonomy
I. Chami, S. Abu-El-Haija, B. Perozzi, C. Ré, and K. Murphy · 2020
Later among the works it cites.
Bridging the gap between spatial and spectral domains: A survey on graph neural networks
Z. Chen et al · 2020
Later among the works it cites.
Sebs: A serverless benchmark suite for function-as-a-service computing
M. Copik, G. Kwasniewski, M. Besta, M. Podstawski, and T. Hoefler · 2020
Later among the works it cites.
Featgraph: A flexible and efficient backend for graph neural network systems
Y. Hu et al · 2020
Later among the works it cites.
Pytorch distributed: Experiences on accelerating data parallel training
S. Li et al · 2020
Later among the works it cites.
Neighborhood and pagerank methods for pairwise link prediction
H. Nassar, A. R. Benson, and D. F. Gleich · 2020
Later among the works it cites.
The future is big graphs! a community view on graph processing systems
S. Sakr et al · 2020
Later among the works it cites.
A survey on the expressive power of graph neural networks
R. Sato · 2020
Later among the works it cites.
Graph neural networks in recommender systems: a survey
S. Wu, F. Sun, W. Zhang, and B. Cui · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
Later among the works it cites.
Agl: a scalable system for industrial-purpose graph machine learning
D. Zhang et al · 2020
Later among the works it cites.
Revisiting graph neural networks for link prediction
M. Zhang, P. Li, Y. Xia, K. Wang, and L. Jin · 2020
Later among the works it cites.
Deep learning on graphs: A survey
Z. Zhang, P. Cui, and W. Zhu · 2020
Later among the works it cites.
Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun · 2020
Later among the works it cites.
Graphminesuite: Enabling high-performance and programmable graph mining algorithms with set algebra
M. Besta et al · 2021
Closest in time.
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
M. Besta et al · 2021
Closest in time.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković · 2021
Closest in time.
Swiss national supercomputing center, 2021
CSCS · 2021
Closest in time.
Learning combinatorial node labeling algorithms
L. Gianinazzi, M. Fries, N. Dryden, T. Ben-Nun, and T. Hoefler · 2021
Closest in time.
Dorylus: Affordable, scalable, and accurate gnn training over billion-edge graphs
J. Thorpe, Y. Qiao, J. Eyolfson, S. Teng, G. Hu, Z. Jia, J. Wei, K. Vora, R. Netravali, M. Kim, et al · 2021
Closest in time.
Seastar: vertex-centric programming for graph neural networks
Y. Wu, K. Ma, Z. Cai, T. Jin, B. Li, C. Zheng, J. Cheng, and F. Yu · 2021
Closest in time.