- 专利标题： MULTI-RELATIONAL GRAPH CONVOLUTIONAL NETWORK (GCN) IN RISK PREDICTION
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申请号： US17241790申请日： 2021-04-27
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公开(公告)号： US20220366231A1公开(公告)日： 2022-11-17
- 发明人: Yada Zhu , Sijia Liu , Aparna Gupta , Sai Radhakrishna Manikant Sarma Palepu , Koushik Kar , Lucian Popa , Kumar Bhaskaran , Nitin Gaur
- 申请人： International Business Machines Corporation , Rensselaer Polytechnic Institute
- 申请人地址： US NY Armonk; US NY Troy
- 专利权人： International Business Machines Corporation,Rensselaer Polytechnic Institute
- 当前专利权人： International Business Machines Corporation,Rensselaer Polytechnic Institute
- 当前专利权人地址： US NY Armonk; US NY Troy
- 主分类号： G06N3/08
- IPC分类号： G06N3/08 ; G06N3/04
摘要：
A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.
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