Abstract
The existing domain-specific methods for mining information networks in machine learning aims to represent the nodes of an information network into a vector format. However, the real-world large-scale information network cannot make well network representations by one network. When the information of the network structure transferred from one network to another network, the performance of network representation might decrease sharply. To achieve these ends, we propose a novel framework to transfer useful information across relational large-scale information networks (FTLSIN). The framework consists of a 2-layer random walks to measure the relations between two networks and predict links across them. Experiments on real-world datasets demonstrate the effectiveness of the proposed model.
Original language | English |
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Title of host publication | 2018 International Joint Conference on Neural Networks, IJCNN 2018 |
Subtitle of host publication | Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-6 |
Number of pages | 6 |
Volume | 2018-July |
ISBN (Electronic) | 9781509060146 |
DOIs | |
Publication status | Published - 10 Oct 2018 |
Externally published | Yes |
Event | 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Conference
Conference | 2018 International Joint Conference on Neural Networks, IJCNN 2018 |
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Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |