Fetching the paper…
Reading the bibliography…
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.
On the evolution of random graphs
Erdos, P. and Rényi, A. (1960) · 1960
Earlier work this paper cites.
Combinatorial Optimization: Algorithms and Complexity
Papadimitriou, C. H. and Steiglitz, K. (1982) · 1982
Earlier work this paper cites.
Graph partitioning using annealed neural networks
Van Den Bout, D. E. and Miller, T. K. (1990) · 1990
Earlier work this paper cites.
Multilevelk-way partitioning scheme for irregular graphs
Karypis, G. and Kumar, V. (1998) · 1998
Earlier work this paper cites.
Multilevel hypergraph partitioning: applications in vlsi domain
Karypis, G., Aggarwal, R., Kumar, V., and Shekhar, S. (1999) · 1999
Earlier work this paper cites.
Multilevel k-way hypergraph partitioning
Karypis, G. and Kumar, V. (2000) · 2000
Earlier work this paper cites.
Normalized cuts and image segmentation
Shi, J. and Malik, J. (2000) · 2000
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Ng, A. Y., Jordan, M. I., and Weiss, Y. (2002) · 2002
Earlier work this paper cites.
Directed scale-free graphs
Bollobás, B., Borgs, C., Chayes, J., and Riordan, O. (2003) · 2003
Earlier work this paper cites.
Local graph partitioning using pagerank vectors
Andersen, R., Chung, F., and Lang, K. (2006) · 2006
Earlier work this paper cites.
Using METIS and hMETIS algorithms in circuit partitioning
Miettinen, P., Honkala, M., and Roos, J. (2006) · 2006
Earlier work this paper cites.
Four proofs for the cheeger inequality and graph partition algorithms
Chung, F. (2007) · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Von Luxburg, U. (2007) · 2007
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Hagberg, A., Swart, P., and S Chult, D. (2008) · 2008
Cited alongside, same era.
Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters
Leskovec, J. (2009) · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Cited alongside, same era.
Powergraph: distributed graph-parallel computation on natural graphs
Gonzalez, J. E., Low, Y., Gu, H., Bickson, D., and Guestrin, C. (2012) · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Cited alongside, same era.
hmetis-based offline road network partitioning
Xu, Y. and Tan, G. (2012) · 2012
Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J. (2017) · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
Later among the works it cites.
Balanced clustering with least square regression
Liu, H., Han, J., Nie, F., and Li, X. (2017) · 2017
Later among the works it cites.
Device placement optimization with reinforcement learning
Mirhoseini, A., Pham, H., Le, Q. V., Steiner, B., Larsen, R., Zhou, Y., Kumar, N., Norouzi, M., Bengio, S., and Dean, J. (2017) · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017) · 2017
Later among the works it cites.
Towards k-means-friendly spaces: Simultaneous deep learning and clustering
Yang, B., Fu, X., Sidiropoulos, N. D., and Hong, M. (2017) · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Balanced k-means and min-cut clustering
Chang, X., Nie, F., Ma, Z., and Yang, Y. (2014) · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Dilokthanakul, N., Mediano, P. A., Garnelo, M., Lee, M. C., Salimbeni, H., Arulkumaran, K., and Shanahan, M. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., and Farhadi, A. (2016) · 2016
Cited alongside, same era.
Variational deep embedding: A generative approach to clustering. arxiv preprint
Zheng, Y., Tan, H., Tang, B., Zhou, H., et al. (2016) · 2016
Cited alongside, same era.
Later among the works it cites.
Scalable minimum-cost balanced partitioning of large-scale social networks: Online and offline solutions
Hada, R. J., Wu, H., and Jin, M. (2018) · 2018
Later among the works it cites.
Mean-field theory of graph neural networks in graph partitioning
Kawamoto, T., Tsubaki, M., and Obuchi, T. (2018) · 2018
Later among the works it cites.
A hierarchical model for device placement
Mirhoseini, A., Goldie, A., Pham, H., Steiner, B., Le, Q. V., and Dean, J. (2018) · 2018
Later among the works it cites.
Spectralnet: Spectral clustering using deep neural networks
Shaham, U., Stanton, K., Li, H., Nadler, B., Basri, R., and Kluger, Y. (2018) · 2018
Later among the works it cites.
Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (2018) · 2018
Later among the works it cites.
Understanding regularized spectral clustering via graph conductance
Zhang, Y. and Rohe, K. (2018) · 2018
Later among the works it cites.
A self-balanced min-cut algorithm for image clustering
Chen, X., Huang, J. Z., Nie, F., Chen, R., and Wu, Q. (2017) · 2088
Closest in time.