Fetching the paper…
Reading the bibliography…
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery.
The pagerank citation ranking: Bringing order to the web
Page, L., Brin, S., Motwani, R., and Winograd, T · 1999
Earlier work this paper cites.
Logistic regression
Kleinbaum, D. G., Dietz, K., Gail, M., Klein, M., and Klein, M · 2002
Earlier work this paper cites.
Network intrusion detection
Northcutt, S., and Novak, J · 2002
Earlier work this paper cites.
Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
Earlier work this paper cites.
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al · 2005
Earlier work this paper cites.
Arnetminer: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z · 2008
Earlier work this paper cites.
Anomaly-based network intrusion detection: Techniques, systems and challenges
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., and Vázquez, E · 2009
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Exploring context and content links in social media: A latent space method
Qi, G.-J., Aggarwal, C., Tian, Q., Ji, H., and Huang, T · 2011
Earlier work this paper cites.
Learning to discover social circles in ego networks
Leskovec, J., and Mcauley, J. J · 2012
Earlier work this paper cites.
A method for local community detection by finding core nodes
Zhang, T., and Wu, B · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
Earlier work this paper cites.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
Earlier work this paper cites.
Heterogeneous network embedding via deep architectures
Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C. C., and Huang, T. S · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., and Salakhutdinov, R · 2015
Cited alongside, same era.
Inferring networks of substitutable and complementary products
McAuley, J., Pandey, R., and Leskovec, J · 2015
Cited alongside, same era.
Deep neural networks for learning graph representations
Cao, S., Lu, W., and Xu, Q · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A., and Leskovec, J · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N., and Welling, M · 2016
Cited alongside, same era.
Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
Later among the works it cites.
Meta-graph: Few shot link prediction via meta learning
Bose, A. J., Jain, A., Molino, P., and Hamilton, W. L · 2019
Later among the works it cites.
Deep anomaly detection on attributed networks
Ding, K., Li, J., Bhanushali, R., and Liu, H · 2019
Later among the works it cites.
Interactive anomaly detection on attributed networks
Ding, K., Li, J., and Liu, H · 2019
Later among the works it cites.
Learning to propagate for graph meta-learning
Liu, L., Zhou, T., Long, G., Jiang, J., and Zhang, C · 2019
Later among the works it cites.
Estimating node importance in knowledge graphs using graph neural networks
Park, N., Kan, A., Dong, X. L., Zhao, T., and Faloutsos, C · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Cited alongside, same era.
Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z., Zhou, F., Chen, F., and Li, H · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S., and Larochelle, H · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Later among the works it cites.
Transductive episodic-wise adaptive metric for few-shot learning
Qiao, L., Shi, Y., Li, J., Wang, Y., Huang, T., and Tian, Y · 2019
Later among the works it cites.
Simplifying graph convolutional networks
Wu, F., Zhang, T., Souza Jr, A. H. d., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
Later among the works it cites.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Later among the works it cites.
Variational few-shot learning
Zhang, J., Zhao, C., Ni, B., Xu, M., and Yang, X · 2019
Later among the works it cites.
Meta-gnn: On few-shot node classification in graph meta-learning
Zhou, F., Cao, C., Zhang, K., Trajcevski, G., Zhong, T., and Geng, J · 2019
Later among the works it cites.
Meta-learning with dynamic-memory-based prototypical network for few-shot event detection
Deng, S., Zhang, N., Kang, J., Zhang, Y., Zhang, W., and Chen, H · 2020
Closest in time.
Inductive anomaly detection on attributed networks
Ding, K., Li, J., Agarwal, N., and Liu, H · 2020
Closest in time.
Next-item recommendation with sequential hypergraphs
Wang, J., Ding, K., Hong, L., Liu, H., and Caverlee, J · 2020
Closest in time.
Graph few-shot learning via knowledge transfer
Yao, H., Zhang, C., Wei, Y., Jiang, M., Wang, S., Huang, J., Chawla, N. V., and Li, Z · 2020
Closest in time.