Language Agnostic Code Embeddings
Saiteja Utpala, Alex Gu, et al.
NAACL 2024
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topological features and automatically partitions the input graph into representative 'stages' for feature learning. The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures. The experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages (unlike a single GAN, which cannot easily identify such features, let alone reconstruct the original graph). This paper is the firstline research on combining the use of GANs and graph topological analysis.
Saiteja Utpala, Alex Gu, et al.
NAACL 2024
Felipe Maia Polo, Lucas Weber, et al.
ICLR 2024
Shuai Zhang, Meng Weng, et al.
NeurIPS 2021
Pin-Yu Chen, Baichuan Zhang, et al.
Social Network Analysis and Mining