Graph classification method of cyclic neural network model based on Attention
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A technology of recurrent neural network and classification method, applied in the field of graph mining, which can solve the problems of lack of adaptability to different sizes of graphs, loss of effective modes, and high computational complexity
Inactive Publication Date: 2019-02-12
ZHEJIANG UNIV
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The size of the subgraph depends on the support threshold setting when mining frequent subgraphs. When the support threshold is set too high, only a small number of extremely frequent subgraphs can be generated, resulting in the loss of effective patterns with high discrimination and affecting the performance of the classifier. When low, it will lead to low efficiency of frequent subgraph mining algorithm
At the same time, a large number of isomorphism detections are required in frequent subgraph mining, and the subgraph isomor...
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Embodiment 1
[0083] In this embodiment, a graph containing 10 nodes is taken as an example to describe in detail the process of moving the view angle based on the idea of Attention in the view angle sensor of the present invention and the encoding of the focus area of the view angle. For this 10-node graph, its vertices are represented by 1, 2, 3, ... 9, 10, and the edges are (1, 2), (1, 5), (2, 3), ( 2, 6), (3, 4), (3, 9), (3, 10), (4, 6), (4, 9), (5, 6), (5, 7), (6, 7), (6, 8), (6, 9), (7, 8), (8, 9), (8, 10), (9, 10), its graph structure and its calculation based on the node vector representation The node ordering neighborhood of Figure 4 and shown in Table 1.
[0084] The central node v observed by the viewing angle sensor from step t-1 t-1 Start moving the vector d according to the viewing angle t-1 Get the node v that the current step should focus on t The process of viewing the view area as the center is based on the view movement of the Attention idea (the view center mov...
Embodiment 2
[0091] This embodiment describes in detail the specific implementation of the graph classification method based on the Attention cyclic neural network in the computer environment of the present invention, and uses public data sets to verify the implementation effect.
[0092] To evaluate the effectiveness of the present invention, five public graph datasets are used for testing. These include three bioinformatics datasets: MUTAG, NCI1, and ENZYMES. MUTAG is a data set with 188 nitro compounds, the category of which indicates whether the compound has a mutagenic effect on bacteria; NCI1 is published by the National Cancer Institute of the United States, which includes 4110 data, and its category indicates that the compound has a mutagenic effect on human tumor cells Whether there is an inhibitory effect; ENZYMES is a protein tertiary structure data set composed of 600 enzymes, and its categories correspond to the classification of 6 enzymes. The other two are social network da...
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Abstract
The invention discloses a graph classification method of a cyclic neural network model based on Attention. The Attension idea is applied in the graph classification problem, and the graph classification problem is regarded as a decision process of interaction between a machine and a graph environment in reinforcement learning. Based on the Attention idea, the machine preferentially observes the target area of the graph classification task instead of directly processing the whole graph, so that the target area can be preferentially processed by ignoring the nodes irrelevant to the classification task, and the visual angle movement direction of the machine observation graph can be trained and determined by reinforcement learning rules. At the same time, the model can control the parameters and computational load, and get rid of the constraint on the size of graph data. The invention constructs a cyclic neural network, which integrates the local information of the graph observed before bythe machine through the hidden layer of the circulating neural network, and is used for assisting the decision of the angle of view movement and the graph classification. The invention avoids the problem of subgraph isomorphism in frequent subgraph mining and the problem that the graph kernel function method lacks scalability.
Description
technical field [0001] The invention belongs to the field of graph mining, and in particular relates to a graph classification method based on an Attention-based cyclic neural network model. Background technique [0002] A graph in graph theory is a graph composed of a number of given points and a line connecting two points. This graph is usually used to describe a certain relationship between certain things. Points represent things, and points connect two points. The line of represents that there is a certain relationship between the corresponding two things. The graph G in graph theory is an ordered pair (V, E), where V is called the vertex set, which is the set of all vertices in the graph, and E is called the edge set, which is the set of edges between all vertices. gather. Simply put, vertices represent things, and edges represent relationships between things. [0003] As a commonly used data structure, graph can accurately model the key characteristics and complex r...
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