.png){kind=icon}
返回
Temporal group-aware graph diffusion networks for dynamic link prediction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2023.103292.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dynamic link prediction is a critical task in network research that seeks to predict future network links based on the relative behavior of prior network changes. However, most existing methods overlook mutual interactions between neighbors and long-distance interactions and lack the interpretability of the model's predictions. To tackle the above issues, in this paper, we propose a temporal group-aware graph diffusion network(TGGDN). First, we construct a group affinity matrix to describe mutual interactions between neighbors, i.e., group interactions. Then, we merge the group affinity matrix into the graph diffusion to form a group-aware graph diffusion, which simultaneously captures group interactions and long-distance interactions in dynamic networks. Additionally, we present a transformer block that models the temporal information of dynamic networks using self-attention, allowing the TGGDN to pay greater attention to task-related snapshots while also providing interpretability to better understand the network evolutionary patterns. We compare the proposed TGGDN with state-of-the-art methods on five different sizes of real-world datasets ranging from 1k to 20k nodes. Experimental results show that TGGDN achieves an average improvement of 8.3% and 3.8% in terms of ACC and AUC on all datasets, respectively, demonstrating the superiority of TGGDN in the dynamic link prediction task.
Keyword:
Dynamic link prediction
Graph convolutional networks
Dynamic graph embedding
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
The role of location and social strength for friendship prediction in location-based social networks基于位置的社交网络中位置和社交强度对友谊预测的作用
OAAI
OAAI分享收藏Oscillatory activity is not evident in the primate temporal visual cortex with static stimuli.png)
NeuroReport
NeuroReportIF0
PREAI
分享收藏Modeling anaerobic digestion of dairy manure using the IWA Anaerobic Digestion Model no. 1 (ADM1)
PREAI
分享收藏FEVERLESS: Fast and Secure Vertical Federated Learning Based on XGBoost for Decentralized LabelsFaverless: 基于XGBoost的快速安全的垂直联邦学习，用于分散标签
PREAI
分享收藏
查看更多内容