Graph neural network: Difference between revisions

Revision as of 08:44, 14 July 2025

Revision as of 13:05, 16 July 2025

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{{See also|Water distribution system}}

Water distribution systems can be modelled as graphs, being then a straightforward application of GNN. This kind of algorithm has been applied to water demand forecasting,<ref>{{cite journal |url=https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022WR032299|title=Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting|last=Zanfei|first=Ariele |display-authors=etal |date=2022|journal=Water Resources Research|volume=58 |issue=7 |publisher=AGU|doi=10.1029/2022WR032299 |bibcode=2022WRR....5832299Z |access-date=June 11, 2024}}</ref> interconnecting District Measuring Areas to improve the forecasting capacity. Other application of this algorithm on water distribution modelling is the development of metamodels.<ref>{{cite journal |url=https://www.sciencedirect.com/science/article/abs/pii/S0043135423007005|title=Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment|last=Zanfei|first=Ariele |journal=Water Research |display-authors=etal |date=2023|volume=242 |article-number=120264 |doi=10.1016/j.watres.2023.120264 |pmid=37393807 |bibcode=2023WatRe.24220264Z |access-date=June 11, 2024}}</ref>

=== Computer Vision ===

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{{See also|Natural language processing}}

Graph-based representation of text helps to capture deeper semantic relationships between words. Many studies have used graph networks to enhance performance in various text processing tasks such as text classification, question answering, Neural Machine Translation (NMT), event extraction, fact verification, etc.<ref>{{Cite journal |last1=Zhou |first1=Jie |last2=Cui |first2=Ganqu |last3=Hu |first3=Shengding |last4=Zhang |first4=Zhengyan |last5=Yang |first5=Cheng |last6=Liu |first6=Zhiyuan |last7=Wang |first7=Lifeng |last8=Li |first8=Changcheng |last9=Sun |first9=Maosong |date=2020-01-01 |title=Graph neural networks: A review of methods and applications |url=https://www.sciencedirect.com/science/article/pii/S2666651021000012 |journal=AI Open |volume=1 |pages=57–81 |doi=10.1016/j.aiopen.2021.01.001 |issn=2666-6510|doi-access=free }}</ref>

==References==

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|url=https://www.nowpublishers.com/article/Details/MAL-096|journal=Foundations and Trends in Machine Learning|volume=16|issue=2|pages=119–328|doi=10.1561/2200000096 |pmid=19068426|s2cid=206756462|issn=1941-0093|arxiv=2106.06090}}</ref>

<ref name="wucuipeizhao2022">{{Cite journal|last1=Wu|first1=Lingfei|last2=Cui|first2=Peng|last3=Pei |first3=Jian|last4=Zhao|first4=Liang|date=2022|title=Graph Neural Networks: Foundations, Frontiers, and Applications|url=https://graph-neural-networks.github.io/|journal=Springer Singapore|pages=725|url-access=<!--WP:URLACCESS-->}}</ref>

<ref name="scarselli2009">{{Cite journal|last1=Scarselli|first1=Franco|last2=Gori|first2=Marco|last3=Tsoi |first3=Ah Chung|last4=Hagenbuchner|first4=Markus|last5=Monfardini|first5=Gabriele|date=2009|title=The Graph Neural Network Model|url=https://ieeexplore.ieee.org/document/4700287|journal=IEEE Transactions on Neural Networks|volume=20|issue=1|pages=61–80|doi=10.1109/TNN.2008.2005605 |pmid=19068426|s2cid=206756462|issn=1941-0093}}</ref>

<ref name="micheli2009">{{Cite journal|last1=Micheli|first1=Alessio|title=Neural Network for Graphs: A Contextual Constructive Approach|url=https://ieeexplore.ieee.org/document/4700287|journal=IEEE Transactions on Neural Networks|year=2009 |volume=20|issue=3|pages=498–511|doi=10.1109/TNN.2008.2010350 |pmid=19193509|s2cid=17486263|issn=1045-9227|url-access=subscription}}</ref>

<ref name="sanchez2021">{{Cite journal|last1=Sanchez-Lengeling|first1=Benjamin|last2=Reif|first2=Emily |last3=Pearce|first3=Adam|last4=Wiltschko|first4=Alex|date=2021-09-02|title=A Gentle Introduction to Graph Neural Networks|url=https://distill.pub/2021/gnn-intro|journal=Distill|volume=6|issue=9|pages=e33 |doi=10.23915/distill.00033|issn=2476-0757|doi-access=free}}</ref>

<ref name="daigavane2021">{{Cite journal|last1=Daigavane|first1=Ameya|last2=Ravindran|first2=Balaraman |last3=Aggarwal|first3=Gaurav|date=2021-09-02|title=Understanding Convolutions on Graphs |url=https://distill.pub/2021/understanding-gnns|journal=Distill|volume=6|issue=9|pages=e32 |doi=10.23915/distill.00032|s2cid=239678898|issn=2476-0757|doi-access=free}}</ref>

<ref name="gilmer2017">{{Cite journal|last1=Gilmer|first1=Justin|last2=Schoenholz|first2=Samuel S. |last3=Riley|first3=Patrick F.|last4=Vinyals|first4=Oriol|last5=Dahl|first5=George E.|date=2017-07-17|title=Neural Message Passing for Quantum Chemistry|url=http://proceedings.mlr.press/v70/gilmer17a.html |journal=Proceedings of Machine Learning Research|language=en|pages=1263–1272|arxiv=1704.01212}}</ref>

<ref name="kipf2016">{{Cite journal|last1=Kipf|first1=Thomas N|last2=Welling|first2=Max|date=2016 |title=Semi-supervised classification with graph convolutional networks|journal=IEEE Transactions on Neural Networks |url=https://ieeexplore.ieee.org/document/4700287 |volume=5|issue=1|pages=61–80 |doi=10.1109/TNN.2008.2005605|pmid=19068426|arxiv=1609.02907|s2cid=206756462}}</ref>

<ref name="hamilton2017">{{Cite journal|last1=Hamilton|first1=William|last2=Ying|first2=Rex |last3=Leskovec|first3=Jure|date=2017|title=Inductive Representation Learning on Large Graphs|url=https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf|journal=Neural Information Processing Systems|volume=31|arxiv=1706.02216|via=Stanford}}</ref>

<ref name="velickovic2018">{{Cite arXiv|last1=Veličković|first1=Petar|last2=Cucurull|first2=Guillem |last3=Casanova|first3=Arantxa|last4=Romero|first4=Adriana|last5=Liò|first5=Pietro|last6=Bengio |first6=Yoshua|date=2018-02-04 |title=Graph Attention Networks|eprint=1710.10903 |class=stat.ML}}</ref>

<ref name=stanforddata>{{Cite web|title=Stanford Large Network Dataset Collection |url=https://snap.stanford.edu/data/|access-date=2021-07-05|website=snap.stanford.edu}}</ref>

<ref name="li2018">{{cite journalbook |last1=Li |first1=Zhuwen |last2=Chen |first2=Qifeng |last3=Koltun |first3=Vladlen |title=CombinatorialNeural optimizationInformation withProcessing graph|chapter=Text convolutionalSimplification networkswith andSelf-Attention-Based guided treePointer-Generator searchNetworks |journalseries=NeuralLecture InformationNotes Processingin SystemsComputer Science |date=2018 |volume=31 |pages=537–546 |doi=10.1007/978-3-030-04221-9_48 |arxiv=1810.10659 |isbn=978-3-030-04220-2 }}</ref>

<ref name="bronstein2021">{{cite arXiv |last1=Bronstein |first1=Michael M. |last2=Bruna |first2=Joan |last3=Cohen |first3=Taco |last4=Veličković |first4=Petar |title=Geometric Deep Learning: Grids, Groups, Graphs Geodesics and Gauges |date=May 4, 2021 |class=cs.LG |eprint=2104.13478}}</ref>

<ref name=douglas2011>{{cite arXiv|last=Douglas|first=B. L.|date=2011-01-27|title=The Weisfeiler–Lehman Method and Graph Isomorphism Testing|class=math.CO|eprint=1101.5211}}</ref>