Structure and Features Fusion with Evidential Graph Convolutional Neural Network for Node Classification
Recently, text-enhanced network representation learning has achieved great success by taking advantage of rich text information and network structure information. However, content-rich network representation learning and quantifying classification uncertainty are challenging when it comes to integrating complex structural dependencies and rich content features at an evidence level. In this paper, we propose an evidential graph representation learning model (EGCN), which can not only fuse network structure and content information into a more complete and powerful representation for each node, but also assess the quality of graph node features to improve classification accuracy. To achieve better fusion, we integrate the node's features representation into structure-aware representation through a delivery operator. Besides, to overcome the difficulty of predicting node classification confidence, we employ a novel module based on Dirichlet distribution theory of evidence and subject opinion learning to collect the evidence of the class probabilities. Experimental results on three real-world networks show that our model can improve both node classification accuracy and robustness as compared to all baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGraph Representation LearningNode ClassificationRepresentation LearningSimilar Papers 제목 키워드 기반
TMF-RSE: Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty for Lung Severity Scoring
Accurate quantification of lung disease severity from chest imaging is critical for clinical decision-making and resource allocation. We propose a tri-modal deep learning framework, TMF-RSE (Tri-Modal Fusion with Regiona…
Fusion of evidential CNN classifiers for image classification
We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extract features from input images and conver…
Classificationimage-classificationImage ClassificationStructural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
Hyperspectral image (HSI) clustering assigns similar pixels to the same class without any annotations, which is an important yet challenging task. For large-scale HSIs, most methods rely on superpixel segmentation and pe…
ClusteringContrastive Learninghyperspectral image clusteringImage Clustering+2The Advantage of Evidential Attributes in Social Networks
Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph struc…
ClusteringEvidential Information Fusion on Possibilistic Structure
Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in h…