{"title":"基于属性融合边缘特征和标签自适应调整的节点分类","authors":"","doi":"10.1016/j.physa.2024.130131","DOIUrl":null,"url":null,"abstract":"<div><div>Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.<!--> <!-->To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).<!--> <!-->Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.<!--> <!-->Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.<!--> <!-->After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.<!--> <!-->Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.<!--> <!-->Each node in the original graph is smoothed together with its neighbor nodes.<!--> <!-->The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.<!--> <!-->This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.<!--> <!-->AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.<!--> <!-->Experimental results show that AFEF_LAA can achieve higher node classification accuracy.</div></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment\",\"authors\":\"\",\"doi\":\"10.1016/j.physa.2024.130131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.<!--> <!-->To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).<!--> <!-->Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.<!--> <!-->Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.<!--> <!-->After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.<!--> <!-->Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.<!--> <!-->Each node in the original graph is smoothed together with its neighbor nodes.<!--> <!-->The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.<!--> <!-->This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.<!--> <!-->AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.<!--> <!-->Experimental results show that AFEF_LAA can achieve higher node classification accuracy.</div></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S037843712400640X\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S037843712400640X","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment
Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings. To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA). Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings. Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes. After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes. Secondly, a multi-fusion method based on smoothed neighborhood information is constructed. Each node in the original graph is smoothed together with its neighbor nodes. The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy. This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data. AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets. Experimental results show that AFEF_LAA can achieve higher node classification accuracy.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.