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Hypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features.
- Source :
-
Entropy . Jan2023, Vol. 25 Issue 1, p89. 16p. - Publication Year :
- 2023
-
Abstract
- Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute information of entities in the actual network, leading to the low performance of the model in link prediction. To mine the cross-modality interactions between the high-order structure and attributes of the network, this paper proposes a hypernetwork link prediction method for fusion topology and attributes (TA-HLP). Firstly, a dual channel coder is employed for jointly learning the structural features and attribute features of nodes. In structural encoding, a node-level attention mechanism is designed to aggregate neighbor information to learn structural patterns effectively. In attribute encoding, the hypergraph is used to refine the attribute features. The high-order relationship between nodes and attributes is modeled based on the node-attribute-node feature update, which preserves the semantic information jointly reflected by nodes and attributes. Moreover, in the joint embedding, a hyperedge-level attention mechanism is introduced to capture nodes with different importance in the hyperedge. Extensive experiments on six data sets demonstrate that this method has achieved a more significant link prediction effect than the existing methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TOPOLOGY
*FORECASTING
*INFORMATION networks
*PREDICTION models
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 25
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 161480076
- Full Text :
- https://doi.org/10.3390/e25010089