Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 60, Issue 3, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2023.103292
Keywords
Dynamic link prediction; Graph convolutional networks; Dynamic graph embedding
Ask authors/readers for more resources
In this paper, a temporal group-aware graph diffusion network (TGGDN) is proposed to address the issues of mutual interactions being overlooked and the lack of interpretability in dynamic network link prediction. Experimental results demonstrate the superiority of TGGDN in the task.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available