Graph TransformerHeterogeneous graphGraph representation learningHeterogeneous graph neural networks (HGNNs) are adept at processing data within multi-relational heterogeneous networks. Nonetheless, contemporary
Heterogeneous graph neural networks (HGNNs) have demonstrated promising capabilities in addressing various problems defined on heterogeneous graphs containing multiple types of nodes or edges. However, traditional HGNN models depend on label information and capture the local structural information of the orig...
图神经网络(GraphNeuralNetworks,GNNs)作为一种新兴的深度学习技术,近年来在推荐系统领域展现出巨大的潜力。传统的推荐系统主要依赖于用户的历史行为和物品的属性进行推荐,但这种基于矩阵分解或深度学习的方法往往忽略了用户和物品之间的复杂关系,以及这些关系中蕴含的丰富信息。GNNs通过在图结构数据上进行信息传播和聚合,能...
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis tasks such as node classification and node clustering. However, most methods ignore the...
摘要: Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural networks (HGNNs) have achieved state-of-the-art perfor...
近几年异质图GNN(Heterogeneous graph neural networks (HGNNs))颇受关注,但是由于每个工作的数据预处理方式和评估设置都不同,因此很难对新模型具体的进步程度做全面理解。 本文使用12个异质图GNN模型的官方代码、数据集、实验设置和超参,证明了它们毫无进展(也不完全,只能说基本没有)。
In recent years, heterogeneous graph neural networks have been applied to the analysis of complex networks, and in ethereum transaction, fraudsters disguise themselves as normal transaction accounts through their behavior, increasing the fault tolerance of using heterogeneous graph neural networks. This pa...
1.2 Graph Neural Networks Definition : General GNN Framework: Suppose H^l[t] is the node representation of node t at the (l)-th GNN layer, the update procedure from the (l-1)-th layer to the (l)-th layer is: H^l[t] \leftarrow \underset{\forall s\in N(t),\ \forall e\in...
【论文解读 CIKM 2018 | GEM】Heterogeneous Graph Neural Networks for Malicious Account Detection,程序员大本营,技术文章内容聚合第一站。
The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, existing methods ignore a tree hierarchy among metapaths, naturally constituted by different node ...