[fix] modify the paper link

docs/source/tutorial/module.rst

@@ -69,7 +69,7 @@ After defining the configuration file, we can firstly read the parameters from t
 Then a :code:`GeneralModel` object is created. If needed, we can adjust the model's parameters by modifying the contents of the configuration file. Here we provide 5 models for continuous-time temporal GNNs:
 Then a :code:`GeneralModel` object is created. If needed, we can adjust the model's parameters by modifying the contents of the configuration file. Here we provide 5 models for continuous-time temporal GNNs:
 
 
 - :code:`TGN`: The TGN model proposed in `Temporal Graph Networks for Deep Learning on Dynamic Graphs <https://arxiv.org/abs/2006.10637>`__.
 - :code:`TGN`: The TGN model proposed in `Temporal Graph Networks for Deep Learning on Dynamic Graphs <https://arxiv.org/abs/2006.10637>`__.
-- :code:`DyRep`: The DyRep model proposed in `Representation Learning and Reasoning on Temporal Knowledge Graphs <https://arxiv.org/abs/2010.02844>`__.
-- :code:`TIGER`: The TIGER model proposed in `TIGER: A Transformer-Based Framework for Temporal Knowledge Graph Completion <https://arxiv.org/abs/2101.00529>`__.
-- :code:`Jodie`: The Jodie model proposed in `JODIE: Joint Optimization of Dynamics and Importance for Online Embedding <https://arxiv.org/abs/1902.10197>`__.
-- :code:`TGAT`: The TGAT model proposed in `Temporal Graph Attention for Deep Temporal Modeling <https://arxiv.org/abs/2002.06902>`__.
+- :code:`DyRep`: The DyRep model proposed in `Representation Learning and Reasoning on Temporal Knowledge Graphs <https://arxiv.org/abs/1803.04051>`__.
+- :code:`TIGER`: The TIGER model proposed in `TIGER: A Transformer-Based Framework for Temporal Knowledge Graph Completion <https://arxiv.org/abs/2302.06057>`__.
+- :code:`Jodie`: The Jodie model proposed in `JODIE: Joint Optimization of Dynamics and Importance for Online Embedding <https://arxiv.org/abs/1908.01207>`__.
+- :code:`TGAT`: The TGAT model proposed in `Temporal Graph Attention for Deep Temporal Modeling <https://arxiv.org/abs/2002.07962>`__.
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