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--- ml:graph_nn [2021/06/06 18:16] – [Papers] jmflanig
+++ ml:graph_nn [2025/04/19 00:20] (current) – [Papers] jmflanig
@@ Line 2: / Line 2: @@
 ===== Overviews =====
-  * [[https://arxiv.org/pdf/1901.00596.pdf|Wu et al 2019 - A Comprehensive Survey on Graph Neural Networks]]
+Best overview: [[https://arxiv.org/pdf/2106.06090.pdf|Wu et al 2021 - Graph Neural Networks for Natural Language Processing: A Survey]]
-  * [[https://www.morganclaypool.com/doi/abs/10.2200/S00980ED1V01Y202001AIM045|Liu  & Zhou 2020 - Introduction to Graph Neural Networks]]
-  * [[https://ieeexplore.ieee.org/document/9046288|Wu et al 2020 - A Comprehensive Survey on Graph Neural Networks]]
+  * General surveys (not just NLP):
-  * {{papers:Graph neural networks A review of methods and applications.pdf|Graph Neural Networks: A Review of Methods and Applications}}
+    * [[https://arxiv.org/pdf/1901.00596.pdf|Wu et al 2019 - A Comprehensive Survey on Graph Neural Networks]]
+    * [[https://www.morganclaypool.com/doi/abs/10.2200/S00980ED1V01Y202001AIM045|Liu  & Zhou 2020 - Introduction to Graph Neural Networks]]
+    * [[https://ieeexplore.ieee.org/document/9046288|Wu et al 2020 - A Comprehensive Survey on Graph Neural Networks]]
+    * {{papers:Graph neural networks A review of methods and applications.pdf|Graph Neural Networks: A Review of Methods and Applications}}
+  * NLP Surveys:
+    * **[[https://arxiv.org/pdf/2106.06090.pdf|Wu et al 2021 - Graph Neural Networks for Natural Language Processing: A Survey]]**
+    * [[https://arxiv.org/pdf/2012.15445.pdf|Yuan et al 2020 - Explainability in Graph Neural Networks: A Taxonomic Survey]]
+    * Nice overview in related work here: [[https://aclanthology.org/2021.findings-acl.126.pdf|Lin 2021]]
 ===== Papers =====
@@ Line 15: / Line 22: @@
   * Graph2Seq
     * [[https://arxiv.org/pdf/1804.00823.pdf|Xu et al 2018 - Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks]]
-  * Graph2Tree
+  * Applications
-    * [[https://www.aclweb.org/anthology/2020.findings-emnlp.255.pdf|Li et al 2020 - Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem]]
+    * See also [[nlp:Knowledge-Enhanced Methods]].
-    * Applications
+    * [[https://www.aclweb.org/anthology/2020.findings-emnlp.255.pdf|Li et al 2020 - Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem]] (Graph to tree neural network)
-      * [[https://www.aclweb.org/anthology/D17-1209v1.pdf|Bastings et al 2017 - Graph Convolutional Encoders for Syntax-aware Neural Machine Translation]]
+    * [[https://www.aclweb.org/anthology/D17-1209v1.pdf|Bastings et al 2017 - Graph Convolutional Encoders for Syntax-aware Neural Machine Translation]]
-      * [[https://arxiv.org/pdf/1905.06241.pdf|Bogin et al 2019 - Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing]]
+    * [[https://arxiv.org/pdf/1905.06241.pdf|Bogin et al 2019 - Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing]]
-      * [[https://arxiv.org/pdf/1908.11214.pdf|Bogin et al 2019 - Global Reasoning over Database Structures for Text-to-SQL Parsing]]
+    * [[https://arxiv.org/pdf/1908.11214.pdf|Bogin et al 2019 - Global Reasoning over Database Structures for Text-to-SQL Parsing]]
-      * [[https://arxiv.org/pdf/2004.13659.pdf|Zhong et al 2020 - LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network]]
+    * [[https://arxiv.org/pdf/2004.13659.pdf|Zhong et al 2020 - LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network]]
-      * [[https://arxiv.org/pdf/1808.09920.pdf|Cao et al 2018 - Question Answering by Reasoning Across Documents with Graph Convolutional Networks]]
+    * [[https://arxiv.org/pdf/1808.09920.pdf|Cao et al 2018 - Question Answering by Reasoning Across Documents with Graph Convolutional Networks]]
-      * [[https://arxiv.org/pdf/1905.07374.pdf|Tu et al 2019 - Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs]]]
+    * [[https://arxiv.org/pdf/1905.07374.pdf|Tu et al 2019 - Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs]]]
-      * [[https://arxiv.org/pdf/1908.00059.pdf|Chen et al 2019 - GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension]]
+    * [[https://arxiv.org/pdf/1908.00059.pdf|Chen et al 2019 - GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension]]
+    * [[https://aclanthology.org/2021.findings-acl.126.pdf|Lin et al 2021 - BertGCN: Transductive Text Classification by Combining GCN and BERT]]
+  * Graph Language Models
+    * [[https://arxiv.org/pdf/2401.07105|2024 - Graph Language Models]]
 ===== Software =====
-  * [[https://github.com/graph4ai/graph4nlp|Graph4NLP]]
+  * [[https://github.com/graph4ai/graph4nlp|Graph4NLP]] (uses DGL as the runtime library)
+  * [[https://www.dgl.ai|DGL - Deep Graph Library]]
+  * [[https://pytorch-geometric.readthedocs.io/en/latest/|PyTorch Geometric]] (not as actively maintained as DGL)
 ===== Tutorials =====
@@ Line 35: / Line 47: @@
 ===== People =====
   * [[https://scholar.google.com/citations?user=FKUc3vsAAAAJ&hl=en|Ivan Titov]]
+  * [[https://scholar.google.com/citations?user=GjcORkUAAAAJ&hl=en|Lingfei Wu]]
 ===== Related Pages =====
+  * [[nlp:Knowledge-Enhanced Methods]]
   * [[nn_architectures|Neural Network Architectures]]