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CBR-FIF: A Novel Dynamic Graph Node Embedding Computation Framework

delete2026-01-01
delete0
PRE
AI
M
Mingjian Ni
G
Gongju Wang
Y
Yinghao Song *
Y
Yang Li
Y
Yan Long
D
Dazhong Li
王彦霏 封面图
王彦霏 (Yanfei Wang)
S
Shikun Zhang
Y
Yulun Song
DOI：10.1007/978-3-031-93257-1_8delete
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摘要

摘要

En 中文
本文提出了一种新颖且可扩展的框架，更充分地利用了动态图的时序上下文信息。动态图节点嵌入是一种将节点嵌入时变图的技术。在执行动态图节点的表示学习时，不仅需要保留图的拓扑结构，还需要保留时序信息。大多数现有方法更侧重于挖掘图的拓扑结构，未能充分利用时序信息。我们引入了一个考虑时序上下文的卷积聚合模块，使得节点嵌入的计算能够同时考虑当前快照及其邻近快照中邻居节点的信息。此外，模型集成了双向长短期记忆网络（BiLSTM）以双向聚合时序数据。最后，针对每个图快照，我们引入了拓扑重建损失函数，以生成更优的节点嵌入。我们在两个任务上进行了实验，结果表明我们的方法优于基线模型。
Keyword:
dynamic graph
node embedding

期刊

C
COLLABORATIVE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING, COLLABORATECOM 2024, PT III
IF:
0
论文数:
14
被引数:
0

机构

P
peking university
学者数:
11.9W
论文数: 8.7W
被引数: 146
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