To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders
This paper investigates the understudied practice of weight sharing (WS) in variational graph autoencoders (VGAE). WS presents both benefits and drawbacks for VGAE model design and node embedding learning, leaving its overall relevance unclear and the question of whether it should be adopted unresolved. We rigorously analyze its implications and, through extensive experiments on a wide range of graphs and VGAE variants, demonstrate that the benefits of WS consistently outweigh its drawbacks. Based on our findings, we recommend WS as an effective approach to optimize, regularize, and simplify VGAE models without significant performance loss.
2025 ACM Web Conference (WWW 2025)
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Social and Information Networks, Machine Learning (cs.LG)
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Social and Information Networks, Machine Learning (cs.LG)
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