7 results about "Simple graph" patented technology

The invention discloses a hypergraph propagation source positioning method based on interaction enhancement. A traditional traceability method mainly depends on a pairwise interaction hypothesis of a simple graph structure, and the positioning precision under a complex topology is limited. The method comprises the following steps: firstly, constructing a hypergraph propagation model and obtaining a complete observation snapshot of a network node; then, extracting the dynamic state and global spectrum features of the nodes, and constructing a basic feature vector; an improved double-flow interaction hypergraph neural network is utilized, hyperedge internal key information is aggregated through local attention flow, and multi-scale topology diffusion flow is utilized in parallel to capture overall long-range dependence; and finally, introducing a gating residual fusion mechanism to adaptively integrate the double-flow features, and accurately outputting the propagation source probability based on a weighted cross entropy loss function. According to the method, the path reconstruction deviation of a traditional method can be effectively overcome and the robustness can be improved in the aspect of processing high-order interaction and long-distance dependence. According to the method, the performance bottleneck based on simple graph traceability is broken through, and powerful technical support is provided for propagation source positioning.

The invention discloses a multi-graph-based multi-communication-path efficient and stable multicast method and device, and relates to the field of computer networks. The method comprises the following steps: modeling a communication network with a plurality of links, and constructing a self-loop-free undirected connected multi-graph; the multi-graph comprises a plurality of nodes and a plurality of edges connected with the nodes, and the edge weight comprises stability and delay; obtaining a stable delay ratio according to the edge weight, and obtaining a simple graph according to the stable delay ratio; creating a multicast tree according to the simple graph; and utilizing the multicast tree to optimize multicast routing. Based on the network topology and the transmission characteristics, the efficient and stable multi-communication-path multicast method is provided, the multicast method can comprehensively consider the characteristics of multiple links between devices, the multicast efficiency and stability are optimized, the stability of the system is improved through multiple links, and therefore the overall performance of data transmission in multicast is improved.

The present application relates to a kind of transformer fault online diagnosis method based on graph neural network.By mechanism analysis to the typical fault of transformer, the characteristic parameter capable of fully representing the fault is obtained, then all characteristic parameters are constructed into a graph network, and the fusion processing of all parameter data is completed.After the establishment of graph network, the dynamic weight of each parameter is continuously trained, then the center node is selected to carry out node aggregation and construct the adjacency matrix of each node, the center node is gradually aggregated from the outer layer, and finally the probability of the occurrence of a certain fault of transformer is output, the construction of graph network needs to continuously train weight.The present application can establish simple graph network using multi-source fusion data to diagnose transformer fault, solve the pain point problem that large sample size leads to difficult data collection and single data type leads to low diagnosis accuracy, in addition, it also combines the correlation between faults under actual operating conditions, and outputs the probability of multiple faults possibly occurring.

This invention discloses a stock price trend prediction method based on multi-level dynamic graph fusion, relating to the fields of financial technology and artificial intelligence data analysis. The method mainly comprises three parts: multi-channel temporal feature extraction, collaborative modeling of macro and micro spatial structures, and multi-level feature fusion prediction. The steps include: First, constructing a multi-channel technical indicator sequence based on historical stock trading data, and using a multi-channel attention pooling GRU network to extract differentiated temporal features in parallel; Second, constructing a stock association hypergraph based on industry and concept labels, introducing graph information loss (GIL) as a feedback signal to drive an adaptive hyperedge reconstruction mechanism, dynamically adjusting hyperedge weights to filter noise and capture macro market hotspots; Simultaneously, constructing a simple graph within the hyperedge and combining contrastive learning constraints to semantically align stocks with similar technical patterns to enhance the discriminative power of micro local features; Finally, integrating macro hypergraph features and micro simple graph features through a cross-graph fusion module, concatenating them with temporal features, and inputting them into the prediction layer to complete the prediction of the stock price trend at the next moment. This method overcomes the limitations of existing static graph models in dynamically capturing high-order stock correlations and fine-grained technical pattern resonances, providing a new approach for financial spatiotemporal data mining.

The invention discloses a truth value calculation method for a simple graph. According to the calculation method, the truth value of the original simple graph is calculated through the truth values of some representative target sub-graphs. Wherein some sub-graphs are selected from the target sub-graphs of one simple graph to form the representative target sub-graph, and the representative target sub-graph is formed by selecting a representative strategy set of the simple graph. The representative strategy set is obtained by selecting some branches from all the connected branches of the simple graph to form a representative connected branch set and then using the strategy set of the representative connected branches to obtain the representative strategy set.

This invention belongs to the field of malicious code detection and provides an Android malware detection system and method based on a hypergraph. It primarily addresses the problems of existing heterogeneous graph-based Android malware detection methods, such as coarse granularity, high computational overhead, and neglect of high-order relationships between applications during application relationship modeling. The main scheme includes: statically analyzing Android APK files to obtain their API call information; constructing a hypergraph describing the relationships between APKs based on their API call relationships; obtaining a simple graph corresponding to the hypergraph through clique expansion; extracting high-order and low-order relationship features between APKs through hypergraph convolution and graph convolution based on the obtained hypergraph, simple graph, and initial node features, and fusing them through an attention mechanism; training and learning the detection model on the fused features using node classification tasks; and using the trained detection model to detect and identify Android malware.

The embodiment of the application provides a network fault positioning method, network equipment and a storage medium, the method comprises the following steps: configuring a link layer discovery protocol in an IP network element and an optical network element, characterizing an adjacency relationship between devices by a simple graph, and constructing a simple graph adjacency matrix of cross-layer association; defining a fault range and a fault device based on an alarm of the simple graph adjacency matrix of cross-layer association, and obtaining association information; and determining a fault position based on the association information. Through the method, not only can the effective interaction of mutual network information and state information between an IP network layer and an optical network layer control plane which lack a cooperation mechanism due to independent operation be solved, but also the positioning problem of an alarm storm of the IP network layer caused by an optical network layer fault can be effectively solved.