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| Link Prediction in Two-layer Knowledge Network Based on Network Representation Learning |
| Cao Zhipeng, Pan Ding, Pan Qiliang |
| Jinan University, Guangzhou 510632 |
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Abstract In recent years, most link prediction algorithms have focused on the similarity of the knowledge network's topological characteristics, with less consideration of the author's research field, which lead to some problems, such as insufficient information utilization. This paper proposes hypernet2vec model, a link prediction model for a two-layer knowledge network. The two-layer knowledge network consists of the Co-author Network and Academic Field Relationship Network. The nodes in the two-layer network are mapped to a low-dimensional vector space by network representation learning, and then they are fed into a convolutional neural network, which is specially designed to calculate and predict future links. The empirical results of the evaluation on real-world networks demonstrate that the proposed algorithm achieves higher AUC (area under curve) value, with an average increase of 11.28%, and performs superior to other algorithms such as RA indicator, LP indicator, and LRW indicator. This paper also explores the underlying mechanism that affects the model's occurrence, from the level of intelligence generation and structure of complex systems.
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Received: 16 December 2019
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