Graph Signal Processing and Graph Machine Learning
图信号处理和图机器学习
基本信息
- 批准号:2884089
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2023
- 资助国家:英国
- 起止时间:2023 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Despite the advancement in e-commerce continuously improving the economy and people's lives, individuals and business often become victims of fraudulent financial transactions with an estimated annual loss of £7 billion and £40 billion respectively. While it is important to develop a highly efficient regulation system to detect and prevent such fraudulent transactions, the complexity and high volumes of financial networks make it difficult to properly model this type of data. In this research, we aim to build effective AI models for financial regulation using machine learning techniques of Graph Neural Networks (GNNs), which model accounts and transactions in thefinancial network as nodes and links, then propagate information through the network. The key characteristic of this method is that when processing information of an individual node (i.e., financial account), the model will also gather information of its neighbours' behaviour (i.e., accounts with transactions) without searching over the entire network, making it an ideal approach to detect fraudulent clusters out of millions of financial transactions. In particular, our research will focus on Representation methods, which essentially work as a filter that can "screen out" the abundant irrelevant information in the financial network, and then use the remaining features as the "representations" to perform down-stream tasks. In addition to fraud detection, our research also works on other regulatory domains of financial networks, with the technical details covered in my research proposal. In credit risk management, we can model various financial entities (e.g., banks, companies, individuals and government) and their relationships (e.g., lending, borrowing and default) as a bank lending network. Then, with the entities' financial history, we can embed them into low-dimensional representations to analyse their behaviours and predict the default probability of loans, which helps lenders to make decision about new loans and monitor existing loans status at the same time. In stock market surveillance, we can treat the accounts and financial products as a bipartition graph with purchase/sell actions being the links among them. Then, by encode the graph into embeddings, we will be able to identify the clusters of entities that are highly correlated or have similar market behaviour, which can help regulators to detect anomalies in the market such as manipulation, insider trading, and other illegal activities.
尽管电子商务的进步不断改善经济和人们的生活,但个人和企业经常成为欺诈性金融交易的受害者,估计每年损失分别为70亿英镑和400亿英镑。虽然开发一个高效的监管系统来检测和防止此类欺诈性交易非常重要,但金融网络的复杂性和高容量使得很难对这类数据进行适当的建模。在本研究中,我们的目标是使用图神经网络(gnn)的机器学习技术为金融监管构建有效的人工智能模型,该模型将金融网络中的账户和交易建模为节点和链接,然后通过网络传播信息。该方法的关键特点是,当处理单个节点(即金融账户)的信息时,该模型还将收集其邻居的行为信息(即有交易的账户),而无需在整个网络中搜索,这使其成为检测数百万金融交易中欺诈集群的理想方法。特别是,我们的研究将集中在表征方法上,它本质上是一个过滤器,可以“筛掉”金融网络中丰富的不相关信息,然后使用剩余的特征作为“表征”来执行下游任务。除了欺诈检测,我们的研究还涉及金融网络的其他监管领域,技术细节在我的研究计划中有涉及。在信用风险管理中,我们可以将各种金融实体(如银行、公司、个人和政府)及其关系(如贷款、借款和违约)建模为银行贷款网络。然后,根据实体的财务历史,我们可以将它们嵌入到低维表示中,分析它们的行为并预测贷款的违约概率,这有助于贷款人对新贷款做出决策,同时监控现有贷款的状态。在股票市场监控中,我们可以把账户和金融产品看作一个以买入/卖出行为作为它们之间的联系的二分图。然后,通过将图编码为嵌入,我们将能够识别高度相关或具有相似市场行为的实体集群,这可以帮助监管机构检测市场中的异常情况,如操纵、内幕交易和其他非法活动。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('', 18)}}的其他基金
An implantable biosensor microsystem for real-time measurement of circulating biomarkers
用于实时测量循环生物标志物的植入式生物传感器微系统
- 批准号:
2901954 - 财政年份:2028
- 资助金额:
-- - 项目类别:
Studentship
Exploiting the polysaccharide breakdown capacity of the human gut microbiome to develop environmentally sustainable dishwashing solutions
利用人类肠道微生物群的多糖分解能力来开发环境可持续的洗碗解决方案
- 批准号:
2896097 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
A Robot that Swims Through Granular Materials
可以在颗粒材料中游动的机器人
- 批准号:
2780268 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Likelihood and impact of severe space weather events on the resilience of nuclear power and safeguards monitoring.
严重空间天气事件对核电和保障监督的恢复力的可能性和影响。
- 批准号:
2908918 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Proton, alpha and gamma irradiation assisted stress corrosion cracking: understanding the fuel-stainless steel interface
质子、α 和 γ 辐照辅助应力腐蚀开裂:了解燃料-不锈钢界面
- 批准号:
2908693 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Field Assisted Sintering of Nuclear Fuel Simulants
核燃料模拟物的现场辅助烧结
- 批准号:
2908917 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Assessment of new fatigue capable titanium alloys for aerospace applications
评估用于航空航天应用的新型抗疲劳钛合金
- 批准号:
2879438 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Developing a 3D printed skin model using a Dextran - Collagen hydrogel to analyse the cellular and epigenetic effects of interleukin-17 inhibitors in
使用右旋糖酐-胶原蛋白水凝胶开发 3D 打印皮肤模型,以分析白细胞介素 17 抑制剂的细胞和表观遗传效应
- 批准号:
2890513 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Understanding the interplay between the gut microbiome, behavior and urbanisation in wild birds
了解野生鸟类肠道微生物组、行为和城市化之间的相互作用
- 批准号:
2876993 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
相似国自然基金
一种检测结核分枝杆菌抗原标志物的方法学研究——基于signal-on型电化学适体检测体系的构建及应用
- 批准号:81601856
- 批准年份:2016
- 资助金额:17.0 万元
- 项目类别:青年科学基金项目
Apoptosis signal-regulating kinase 1是七氟烷抑制小胶质细胞活化的关键分子靶点?
- 批准号:81301123
- 批准年份:2013
- 资助金额:23.0 万元
- 项目类别:青年科学基金项目
相似海外基金
Graph based signal processing for optical networks
光网络基于图的信号处理
- 批准号:
2737255 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Studentship
QuantSLEEP: A numerical toolbox for a graph signal processing of the rhythmic and arrhythmic components of sleep EEG recordings.
QuantSLEEP:一个数字工具箱,用于对睡眠脑电图记录的节律和心律失常成分进行图形信号处理。
- 批准号:
RGPIN-2022-05351 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Discovery Grants Program - Individual
Advancing Graph Signal Processing Techniques for Monitoring and Control of Electric Distribution Power Systems
先进的图形信号处理技术用于配电电力系统的监测和控制
- 批准号:
2210012 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Standard Grant
CAREER: Bayesian Graph Signal Processing for Machine Perception
职业:用于机器感知的贝叶斯图信号处理
- 批准号:
2146261 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Continuing Grant
CCSS: Hyper-Graph Signal Processing for Multimedia Data Analysis in Cyber System Applications
CCSS:用于网络系统应用中多媒体数据分析的超图信号处理
- 批准号:
2029848 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Standard Grant
Integration of graph signal processing and hyperspectral image processing
图信号处理和高光谱图像处理的集成
- 批准号:
21K21312 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Grant-in-Aid for Research Activity Start-up
Signal Processing Over Networks: Graph-Based Methods for Data Analysis
网络信号处理:基于图的数据分析方法
- 批准号:
RGPIN-2017-06266 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Discovery Grants Program - Individual
A Graph Signal Processing Framework for Situational Awareness in Smart Grids
用于智能电网态势感知的图形信号处理框架
- 批准号:
2118510 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Standard Grant
Signal Processing Over Networks: Graph-Based Methods for Data Analysis
网络信号处理:基于图的数据分析方法
- 批准号:
RGPIN-2017-06266 - 财政年份:2020
- 资助金额:
-- - 项目类别:
Discovery Grants Program - Individual