Content deleted Content added
m Open access bot: url-access=subscription updated in citation with #oabot. |
Citation bot (talk | contribs) Add: article-number, bibcode. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 69/990 |
||
Line 150:
=== Cyber security ===
{{See also|Intrusion detection system}}
When viewed as a graph, a network of computers can be analyzed with GNNs for anomaly detection. Anomalies within provenance graphs often correlate to malicious activity within the network. GNNs have been used to identify these anomalies on individual nodes<ref>{{Cite journal |last1=Wang |first1=Su |last2=Wang |first2=Zhiliang |last3=Zhou |first3=Tao |last4=Sun |first4=Hongbin |last5=Yin |first5=Xia |last6=Han |first6=Dongqi |last7=Zhang |first7=Han |last8=Shi |first8=Xingang |last9=Yang |first9=Jiahai |date=2022 |title=Threatrace: Detecting and Tracing Host-Based Threats in Node Level Through Provenance Graph Learning |url=https://ieeexplore.ieee.org/document/9899459/;jsessionid=NzAXdLahhjEX-xmrFzOROk4qxoaz40aJFvKcZRgjck8-zCOucJi7!380715771 |journal=IEEE Transactions on Information Forensics and Security |volume=17 |pages=3972–3987 |doi=10.1109/TIFS.2022.3208815 |issn=1556-6021|arxiv=2111.04333 |bibcode=2022ITIF...17.3972W |s2cid=243847506 }}</ref> and within paths<ref>{{Cite journal |last1=Wang |first1=Qi |last2=Hassan |first2=Wajih Ul |last3=Li |first3=Ding |last4=Jee |first4=Kangkook |last5=Yu |first5=Xiao |date=2020 |title=You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis. |journal=Network and Distributed Systems Security Symposium|doi=10.14722/ndss.2020.24167 |isbn=978-1-891562-61-7 |s2cid=211267791 |doi-access=free }}</ref> to detect malicious processes, or on the edge level<ref>{{Cite journal |last1=King |first1=Isaiah J. |last2=Huang |first2=H. Howie |date=2022 |title=Euler: Detecting Network Lateral Movement via Scalable Temporal Link Prediction |url=https://www.ndss-symposium.org/wp-content/uploads/2022-107A-paper.pdf |journal=In Proceedings of the 29th Network and Distributed Systems Security Symposium|doi=10.14722/ndss.2022.24107 |s2cid=248221601 }}</ref> to detect [[Network Lateral Movement|lateral movement]].
=== Water distribution networks ===
{{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 |article-number=e2022WR032299 |publisher=AGU|doi=10.1029/2022WR032299 |bibcode=2022WRR....5832299Z |access-date=11 June 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=11 June 2024|url-access=subscription }}</ref>
=== Computer Vision ===
Line 172:
|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|journal=IEEE Transactions on Neural Networks|volume=20|issue=1|pages=61–80|doi=10.1109/TNN.2008.2005605 |pmid=19068426|bibcode=2009ITNN...20...61S |s2cid=206756462|issn=1941-0093}}</ref>
<ref name="micheli2009">{{Cite journal|last1=Micheli|first1=Alessio|title=Neural Network for Graphs: A Contextual Constructive Approach|journal=IEEE Transactions on Neural Networks|year=2009 |volume=20|issue=3|pages=498–511|doi=10.1109/TNN.2008.2010350 |pmid=19193509|bibcode=2009ITNN...20..498M |s2cid=17486263|issn=1045-9227}}</ref>
<ref name="sanchez2021">{{Cite journal|last1=Sanchez-Lengeling|first1=Benjamin|last2=Reif|first2=Emily |last3=Pearce|first3=Adam|last4=Wiltschko|first4=Alex|date=2 September 2021|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=2 September 2021|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=17 July 2017|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 |volume=5|issue=1|pages=61–80 |doi=10.1109/TNN.2008.2005605|pmid=19068426|arxiv=1609.02907|bibcode=2009ITNN...20...61S |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=4 February 2018 |title=Graph Attention Networks|eprint=1710.10903 |class=stat.ML}}</ref>
| |||