Recently, graph convolutional networks (GCNs) have been employed for graph matching
problem. It can integrate graph node feature embedding, node-wise affinity learning and …

Graph neural networks (GNNs) technology has been widely used in recommendation
systems because most information in recommendation systems has a graph structure in …

Softassign is a pivotal method in graph matching and other learning tasks. Many softassign-
based algorithms exhibit performance sensitivity to a parameter in the softassign. However …

The CAD model retrieval has played a significant role in various applications, including
product development and knowledge mining. However, most existing retrieval methods …

Most of the existing Transformer-based models have been shown to have great advantages
in sequential recommendation by modeling temporal dynamics through the self-attention …

This paper presents an effective dissimilarity measure for geometric graphs representing
shapes. The proposed dissimilarity measure is a distance that combines a sparsification of …

The traditional methods of forming organization management structures are that the existing
management structures are adjusted to the tasks of project management and continuous …

The subgraph-subgraph matching problem is, given a pair of graphs and a positive integer $
K $, to find $ K $ vertices in the first graph, $ K $ vertices in the second graph, and a bijection …

Graph matching aims to find correspondences between two graphs. As a fundamental
problem in computer vision and pattern recognition, it is widely used in shape matching …