Fetching the paper…
Reading the bibliography…
Event detection is a critical task for timely decision-making in graph analytics applications.
A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2019 · 1901
Earlier work this paper cites.
Jain, S.; and Wallace, B. C. 2019 · 1902
Earlier work this paper cites.
Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Pareja, A.; Domeniconi, G.; Chen, J.; Ma, T.; Suzumura, T.; Kanezashi, H.; Kaler, T.; and Leisersen, C. E. 2019 · 1902
Earlier work this paper cites.
Node embedding over temporal graphs
Singer, U.; Guy, I.; and Radinsky, K. 2019 · 1903
Earlier work this paper cites.
Attention is not not explanation
Wiegreffe, S.; and Pinter, Y. 2019 · 1908
Earlier work this paper cites.
Learning to deceive with attention-based explanations
Pruthi, D.; Gupta, M.; Dhingra, B.; Neubig, G.; and Lipton, Z. C. 2019 · 1909
Earlier work this paper cites.
GrIDS-a graph based intrusion detection system for large networks
Staniford-Chen, S.; Cheung, S.; Crawford, R.; Dilger, M.; Frank, J.; Hoagland, J.; Levitt, K.; et al. 1996 · 1996
Earlier work this paper cites.
Clustering intrusion detection alarms to support root cause analysis
Julisch, K. 2003 · 2003
Earlier work this paper cites.
Marginalized kernels between labeled graphs
Kashima, H.; Tsuda, K.; and Inokuchi, A. 2003 · 2003
Earlier work this paper cites.
A new model for learning in graph domains
Gori, M.; Monfardini, G.; and Scarselli, F. 2005 · 2005
Earlier work this paper cites.
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Skarding, J.; Gabrys, B.; and Musial, K. 2020 · 2005
Earlier work this paper cites.
An introduction to ROC analysis
Fawcett, T. 2006 · 2006
Earlier work this paper cites.
Supervision patterns in discrete event systems diagnosis
Jéron, T.; Marchand, H.; Pinchinat, S.; and Cordier, M.-O. 2006 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Shervashidze, N.; Vishwanathan, S.; Petri, T.; Mehlhorn, K.; and Borgwardt, K. 2009 · 2009
Earlier work this paper cites.
Event detection in time series of mobile communication graphs
Akoglu, L.; and Faloutsos, C. 2010 · 2010
Earlier work this paper cites.
A theoretical analysis of feature pooling in visual recognition
Boureau, Y.-L.; Ponce, J.; and LeCun, Y. 2010 · 2010
Earlier work this paper cites.
Earthquake shakes Twitter users: real-time event detection by social sensors
Sakaki, T.; Okazaki, M.; and Matsuo, Y. 2010 · 2010
Earlier work this paper cites.
Network structure and community evolution on twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami
Lu, X.; and Brelsford, C. 2014 · 2011
Earlier work this paper cites.
Event detection in social streams
Aggarwal, C. C.; and Subbian, K. 2012 · 2012
Earlier work this paper cites.
On the use of cross-validation for time series predictor evaluation
Bergmeir, C.; and Benítez, J. M. 2012 · 2012
Earlier work this paper cites.
Subgraph matching kernels for attributed graphs
Kriege, N.; and Mutzel, P. 2012 · 2012
Cited alongside, same era.
Gdelt: Global data on events, location, and tone, 1979–2012
Leetaru, K.; and Schrodt, P. A. 2013 · 2012
Cited alongside, same era.
Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
Cited alongside, same era.
Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs
Chen, F.; and Neill, D. B. 2014 · 2014
Cited alongside, same era.
’Beating the news’ with EMBERS: forecasting civil unrest using open source indicators
Ramakrishnan, N.; Butler, P.; Muthiah, S.; Self, N.; Khandpur, R.; Saraf, P.; Wang, W.; Cadena, J.; Vullikanti, A.; et al. 2014 · 2014
Cited alongside, same era.
STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting
Ning, Y.; Tao, R.; Reddy, C. K.; Rangwala, H.; Starz, J. C.; and Ramakrishnan, N. 2018 · 2018
Later among the works it cites.
Structured sequence modeling with graph convolutional recurrent networks
Seo, Y.; Defferrard, M.; Vandergheynst, P.; and Bresson, X. 2018 · 2018
Later among the works it cites.
Spatial temporal graph convolutional networks for skeleton-based action recognition
Yan, S.; Xiong, Y.; and Lin, D. 2018 · 2018
Later among the works it cites.
Hierarchical graph representation learning with differentiable pooling
Ying, R.; You, J.; Morris, C.; Ren, X.; Hamilton, W. L.; and Leskovec, J. 2018 · 2018
Later among the works it cites.
An end-to-end deep learning architecture for graph classification
Zhang, M.; Cui, Z.; Neumann, M.; and Chen, Y. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rozenshtein, P.; Anagnostopoulos, A.; Gionis, A.; and Tatti, N. 2014 · 2014
Cited alongside, same era.
A survey of techniques for event detection in twitter
Atefeh, F.; and Khreich, W. 2015 · 2015
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K.; Maclaurein, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015 · 2015
Cited alongside, same era.
Detecting change points in the large-scale structure of evolving networks
Peel, L.; and Clauset, A. 2015 · 2015
Cited alongside, same era.
Anomaly detection in dynamic networks: a survey
Ranshous, S.; Shen, S.; Koutra, D.; Harenberg, S.; Faloutsos, C.; and Samatova, N. F. 2015 · 2015
Cited alongside, same era.
Less is more: Building selective anomaly ensembles with application to event detection in temporal graphs
Rayana, S.; and Akoglu, L. 2015 · 2015
Cited alongside, same era.
Deep learning
Goodfellow, I.; Bengio, Y.; and Courville, A. 2016 · 2016
Cited alongside, same era.
Learning Dynamic Context Graphs for Predicting Social Events
Deng, S.; Rangwala, H.; and Ning, Y. 2019 · 2019
Later among the works it cites.
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S.; Lin, Y.; Feng, N.; Song, C.; and Wan, H. 2019 · 2019
Later among the works it cites.
Semi-supervised graph classification: A hierarchical graph perspective
Li, J.; Rong, Y.; Cheng, H.; Meng, H.; Huang, W.; and Huang, J. 2019 · 2019
Later among the works it cites.
Temporal network embedding with micro-and macro-dynamics
Lu, Y.; Wang, X.; Shi, C.; Yu, P. S.; and Ye, Y. 2019 · 2019
Later among the works it cites.
Recurrent Space-time Graph Neural Networks
Nicolicioiu, A.; Duta, I.; and Leordeanu, M. 2019 · 2019
Later among the works it cites.
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models
Taheri, A.; Gimpel, K.; and Berger-Wolf, T. 2019 · 2019
Later among the works it cites.
DyRep: Learning Representations over Dynamic Graphs
Trivedi, R.; Farajtabar, M.; Biswal, P.; and Zha, H. 2019 · 2019
Later among the works it cites.
Gnnexplainer: Generating explanations for graph neural networks
Ying, R.; Bourgeois, D.; You, J.; Zitnik, M.; and Leskovec, J. 2019 · 2019
Later among the works it cites.
T-gcn: A temporal graph convolutional network for traffic prediction
Zhao, L.; Song, Y.; Zhang, C.; Liu, Y.; Wang, P.; Lin, T.; Deng, M.; and Li, H. 2019 · 2019
Later among the works it cites.
A Fair Comparison of Graph Neural Networks for Graph Classification
Errica, F.; Podda, M.; Bacciu, D.; and Micheli, A. 2020 · 2020
Later among the works it cites.
The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling
Ho, Y.; and Wookey, S. 2020 · 2020
Later among the works it cites.
A survey on graph kernels
Kriege, N. M.; Johansson, F. D.; and Morris, C. 2020 · 2020
Later among the works it cites.
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
Sankar, A.; Wu, Y.; Gou, L.; Zhang, W.; and Yang, H. 2020 · 2020
Later among the works it cites.
Graph deep learning: State of the art and challenges
Georgousis, S.; Kenning, M. P.; and Xie, X. 2021 · 2021
Closest in time.
Time-Series Event Prediction with Evolutionary State Graph
Hu, W.; Yang, Y.; Cheng, Z.; Yang, C.; and Ren, X. 2021 · 2021
Closest in time.