Network embedding method based on attribute and structure deep fusion and model thereof
The invention discloses a network embedding method based on attribute and structure deep fusion. The network embedding method comprises the following steps: S1, obtaining node attribute characteristics of a reconstructed coding layer; S2, obtaining a node attribute information sequence with node att...
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| creator | ZHANG XIANKUN LUO XUEXIONG MA YUNBIN |
| description | The invention discloses a network embedding method based on attribute and structure deep fusion. The network embedding method comprises the following steps: S1, obtaining node attribute characteristics of a reconstructed coding layer; S2, obtaining a node attribute information sequence with node attribute characteristics; and S3, translating the node attribute information sequence into a node identity sequence to obtain node embedded vector representation capable of retaining the network structure and the node attribute information of the original network. The invention further discloses a network embedding model based on attribute and structure deep fusion. The network embedding model comprises a multi-modal attribute sensing module, an attribute embedding layer and a multi-hop structuresensing module. The multi-mode attribute sensing module is connected with the multi-hop structure sensing module through the attribute embedding layer. According to the network embedding method basedon attribute and structure |
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The network embedding method comprises the following steps: S1, obtaining node attribute characteristics of a reconstructed coding layer; S2, obtaining a node attribute information sequence with node attribute characteristics; and S3, translating the node attribute information sequence into a node identity sequence to obtain node embedded vector representation capable of retaining the network structure and the node attribute information of the original network. The invention further discloses a network embedding model based on attribute and structure deep fusion. The network embedding model comprises a multi-modal attribute sensing module, an attribute embedding layer and a multi-hop structuresensing module. The multi-mode attribute sensing module is connected with the multi-hop structure sensing module through the attribute embedding layer. 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The network embedding method comprises the following steps: S1, obtaining node attribute characteristics of a reconstructed coding layer; S2, obtaining a node attribute information sequence with node attribute characteristics; and S3, translating the node attribute information sequence into a node identity sequence to obtain node embedded vector representation capable of retaining the network structure and the node attribute information of the original network. The invention further discloses a network embedding model based on attribute and structure deep fusion. The network embedding model comprises a multi-modal attribute sensing module, an attribute embedding layer and a multi-hop structuresensing module. The multi-mode attribute sensing module is connected with the multi-hop structure sensing module through the attribute embedding layer. According to the network embedding method basedon attribute and structure</abstract><oa>free_for_read</oa></addata></record> |
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| language | chi ; eng |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Network embedding method based on attribute and structure deep fusion and model thereof |
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