.png){kind=icon}
返回
A deeper look at Graph Embedding RetroFitting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jocs.2023.101979.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Representation learning for graphs has attracted increasing attention in recent years. In particular, this work is focused on a new problem in this realm which is learning attributed graph embeddings. The setting considers how to update existing node representations from structural graph embedding methods when some additional node attributes are given. Recently, Graph Embedding RetroFitting (GERF) Bielak et al. was proposed to this end - a method that delivers a compound node embedding that follows both the graph structure and attribute space similarity. It uses existing structural node embeddings and retrofits them according to the neighborhood defined by the node attributes space (by optimizing the invariance loss and the attribute neighbor loss). In order to refine GERF method, we aim to include the simplification of the objective function and provide an algorithm for automatic hyperparameter estimation, whereas the experimental scenario is extended by a more robust hyperparameter search for all considered methods and a link prediction problem for evaluation of node embeddings.
Keyword:
Graph embedding
Attributed graphs
Retrofitting
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
.png){kind=icon}
OAAI分享收藏Analysis and assessment of ship collision accidents using Fault Tree and Multiple Correspondence Analysis基于故障树和多重对应分析的船舶碰撞事故分析与评估
PREAI
分享收藏Developing Wind and/or Solar Powered Crop Irrigation Systems for the Great Plains为大平原开发风能和/或太阳能作物灌溉系统
PREAI
分享收藏Inosine pranobex enhances human NK cell cytotoxicity by inducing metabolic activation and NKG2D ligand expression肌苷pranobex通过诱导代谢活化和NKG2D配体表达增强人NK细胞的细胞毒性OAAI
OAAI分享收藏 没有更多内容
没有更多内容