Multi-view comparative learning-based citation network graph representation learning system and method
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A technology of learning system and network diagram, applied in the field of citation network diagram representation learning system based on multi-view contrast learning, can solve the problems of ignoring position information, confusion, limited recognition ability of diagram representation, etc. Effect
Pending Publication Date: 2021-11-09
ZHEJIANG NORMAL UNIVERSITY
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This mutual information evaluation method has the following two problems. 1) It only pays attention to the graph structure information from a single perspective, ignoring the position information of subgraph structures in different ranges; 2) it extracts all node information indiscriminately, making difficult samples Difficult to distinguish
Through the above method, the network usually prefers to fit the overall or extreme local graph representation, and it will cause confusion when identifying difficult nodes, which will lead to inaccurate estimation of the similarity between node representations, and the learned graph representation recognition ability is limited.
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Embodiment 1
[0075] This embodiment provides a citation network graph representation learning system based on multi-view comparative learning, including:
[0076] The sample construction module 11 is used to represent the corresponding original graph node in the citation network graph as a positive sample, and construct a negative corresponding sample based on the original graph;
[0077] The graph enhancement module 12 is used to enhance the graph structure and initial node features in the positive sample based on the personalized page ranking algorithm and the Laplac smoothing algorithm, and obtain positive sample graphs and negative sample graphs related to the positive sample node set;
[0078] The fusion module 13 is used to extract positive sample graph representations and negative sample graph representations based on the GCN encoder, integrate positive sample graph representations and negative sample graph representations, and centralize the fusion layer through cross-views to obtai...
Embodiment 2
[0147] The difference between the citation network graph representation learning system based on multi-view comparative learning provided in this embodiment and the first embodiment is that:
[0148] To evaluate the effectiveness of the proposed MHGI, extensive experiments are conducted on six widely used datasets, including Cora1, Citeseer1, Pubmed1, Amap2, Amac2 and Corafull3. For the Cora, CiteSeer and PubMed datasets, the same train / validation / test split was used as in [Thomas N.Kipf and MaxWelling.2017.Semi-Supervised Classification with Graph ConvolutionalNetworks.InProceedings of the International Conference on Learning Representations]. For the other three datasets (i.e. Amap, Amac, and Corafull), since they have no common split criteria available, random splits are used, where 7%, 7%, and the remaining 86% of nodes are selected for training, validation, and testing, respectively set.
[0149] Table 1 shows the comparison of node classification accuracy of different m...
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Abstract
The invention discloses a multi-view comparative learning-based citation network graph representation learning system and method. The citation network graph representation learning system comprises: a sample construction module which takes original graph node representation as a positive sample and constructs a negative corresponding sample based on an original graph; a graph enhancement module which is used for enhancing the positive sample node features based on a personalized page ranking algorithm and a Laplace smoothing algorithm to obtain a positive sample graph and a negative sample graph; a fusion module which is used for extracting the positive sample graph representation and the negative sample graph representation based on an encoder, integrating the positive sample graph representation and the negative sample graph representation, and obtaining the consensus representation of the positive sample graph and the negative sample graph through a cross view concentrated fusion layer; a mutual information estimation module which is used for comparing the learning representations of the positive sample pair and the negative sample pair through a discriminator; and a difficult sample mining module which represents the consistency between the negative sample pairs according to a pre-calculated affinity vector, and selects and retains nodes which are difficult to express global or neighbor information.
Description
technical field [0001] The invention relates to the technical field of citation network-oriented graph representation learning, in particular to a citation network graph representation learning system and method based on multi-view comparison learning. Background technique [0002] With the development of informatization, the continuous increase of storage space, and the continuous expansion of Internet user groups, a large amount of literature information is generated every day. How to use these literatures to better serve different technical industries and a large number of groups has become the focus of researchers. Content. A citation network refers to a collection of citation and cited relationships between documents, including scientific journals, patent documents, conference proceedings, scientific reports, and dissertations. A large amount of English data exists in various applications in real life. By analyzing it in different dimensions, it can help users understa...
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