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The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
Intensitätsschwankungen im Fernsprechverkehr
C. Palm · 1943
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Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes · 1971
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Space-time point-process models for earthquake occurrences
Yosihiko Ogata · 1998
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An introduction to the theory of point processes: volume II: general theory and structure
Daryl J Daley and David Vere-Jones · 2007
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Survival and event history analysis: a process point of view
Odd Aalen, Ornulf Borgan, and Hakon Gjessing · 2008
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An introduction to numerical analysis
Kendall E Atkinson · 2008
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Link prediction based on local random walk
Weiping Liu and Linyuan Lü · 2010
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
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Introducing the knowledge graph: things, not strings
Amit Singhal · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Gdelt: Global data on events, location, and tone, 1979–2012
Kalev Leetaru and Philip A Schrodt · 2013
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Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes
Ke Zhou, Hongyuan Zha, and Le Song · 2013
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Learning to propagate knowledge in web ontologies
Pasquale Minervini, Claudia d’Amato, Nicola Fanizzi, and Volker Tresp · 2014
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Icews coded event data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward · 2015
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Estimation of slowly decreasing hawkes kernels: application to high-frequency order book dynamics
Emmanuel Bacry, Thibault Jaisson, and Jean-François Muzy · 2016
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Recurrent marked temporal point processes: Embedding event history to vector
Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song · 2016
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Predicting the co-evolution of event and knowledge graphs
Knowledge graph completion via complex tensor factorization
Théo Trouillon, Christopher R Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, and Guillaume Bouchard · 2017
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Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
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Learning sequence encoders for temporal knowledge graph completion
Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert · 2018
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Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol · 2018
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Dynamic graph representation learning via self-attention networks
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang · 2018
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Towards time-aware knowledge graph completion
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Bayesian poisson tucker decomposition for learning the structure of international relations
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The neural hawkes process: A neurally self-modulating multivariate point process
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Holistic representations for memorization and inference
Yunpu Ma, Marcel Hildebrandt, Volker Tresp, and Stephan Baier
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Modeling relational data with graph convolutional networks
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Recurrent event network: Global structure inference over temporal knowledge graph
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Time2vec: Learning a vector representation of time
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Pytorch: An imperative style, high-performance deep learning library
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Rotate: Knowledge graph embedding by relational rotation in complex space
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Temporal knowledge graph embedding model based on additive time series decomposition
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