6 results about "Network representation learning" patented technology

The present disclosure proposes a network representation learning method across medical data sources, comprising: S1, generating medical network data comprising a source network and a target network; S2, randomly sampling a set number of nodes from the source network and the target network; S3, obtaining an L-layer neural network, and calculating the structural features and expression features of the source network and the target network respectively for each layer, and calculating the distance loss between the network features of the source network and the target network; S4, obtaining the output of the source network in the L-layer neural network, and calculating the loss value according to the classification loss and the distance loss, and updating the parameters of the algorithm according to the back propagation algorithm; S5, repeating steps S2-S4 until the entire algorithm converges, so that the accuracy of the algorithm for disease classification no longer rises within multiple iterations. The present disclosure considers the problem of inconsistent data distribution between different hospital data sources, and through the extraction of network structural information and node attribute information and the minimization of feature distance, the information loss is compensated, which has a wide application space.

The application discloses a point of interest recommendation method based on meta-path enhanced view contrast learning, and the method comprises the following steps: constructing an initial position social network and generating node initial features; determining and generating corresponding enhanced edges according to whether the geographical distance between node pairs based on different correlation relationships meets the corresponding preset threshold, so as to obtain an enhanced position social network; constructing a network mode view, a meta-path view and a meta-path enhanced view; performing meta-path level view contrast and graph level view contrast, and respectively calculating corresponding contrast losses; calculating the total loss of model training by linearly weighting the meta-path level contrast loss and the graph level contrast loss; and generating a user point of interest recommendation after optimizing model parameters. The application realizes the construction of enhanced edges by integrating time and space information to alleviate data sparsity, and improves the network representation learning quality and the point of interest recommendation accuracy by means of multi-level feature aggregation and multi-level view contrast.

The application discloses a multi-center depression recognition method and system based on decoupling of cross-subject relationship networks, relates to the technical field of medical image processing and artificial intelligence diagnosis, and comprises the following steps: acquiring subject data of a plurality of collection centers, wherein the subject data comprises brain image data; constructing an initial individual brain function network based on the brain image data; performing individual brain network representation learning on the initial individual brain function network to obtain a subject-level brain network representation; and performing decoupling learning and joint optimization based on disease-related and collection center-related cross-subject relationship networks, and then guiding iterative updating of the individual brain network through a group-level disease discrimination representation, so that the common characteristics related to depression can be better mined, the influence of the differences between the collection centers on feature learning is weakened, the representation and discrimination ability of the model for disease characteristics is improved, the model has more stable performance in a multi-center scene, and the generalization performance of cross-center data is improved.

The application discloses a multi-head attention mechanism-based super network representation learning method applied to breeding, relates to the technical field of crop breeding, and comprises the following steps: constructing a crop heterogeneous super network, and constructing an initial embedding matrix corresponding to all nodes; obtaining a normalized node representation vector corresponding to each tuple and a similarity score of each tuple; calculating the pairwise similarity between each two nodes; constructing a joint loss function, and optimizing the parameters of a target model; generating a final representation vector of a node in the crop heterogeneous super network by using the target model after parameter optimization; and predicting a crop variety according to the final representation vector. The application avoids complex graph conversion and explicit high-order combination calculation, improves the calculation efficiency and scalability, can more comprehensively and accurately encode semantic and structural information of phenotypic traits, and thus provides reliable and high-quality data basis for crop variety prediction based on the final representation vector.

The application discloses a kind of based on residual unit optimization's super-network representation learning method applied to breeding, it is related to crop breeding technical field, method includes: constructing crop heterogeneous super-network, obtaining the representation vector of each node, and obtaining at least one to be modeled high-order tuple, the node representation sequence corresponding to each to be modeled high-order tuple is input into target model, the similarity of each to be modeled high-order tuple is obtained;Based on the similarity of each to be modeled high-order tuple, the representation vector of each node in crop heterogeneous super-network is optimized;Using the representation vector of all nodes after optimization, the prediction of crop variety is carried out.The application solves the problem that the existing method is insufficient in super-edge fusion, thereby improving the depth and fidelity of learning representation from high-order structure information, and while ensuring the ability to model complex relationships, the operability and pertinence of the method in real breeding scenarios are improved.

The present disclosure provides a network representation learning method, device and equipment and a storage medium, wherein the network representation learning method utilizes a target neural network to perform network representation learning in a target field; the target neural network comprises N-level first neural networks and N-1-level second neural networks; the output of the t-level first neural network is the input of the t-level second neural network; the output of the (t-1)-level second neural network is the input of the t-level first neural network and the input of the t-level second neural network; the network representation learning method comprises: obtaining heterogeneous network data; inputting the heterogeneous network data into the target neural network to obtain a first network representation; determining training data based on the heterogeneous network data and the first network representation; training a logistic regression model and the target neural network based on the training data; and taking the second network representation output by the target neural network when the training of the logistic regression model and the target neural network is completed as a target network representation of a target field task.