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Topological Graph Signal Compression
- Publication Year :
- 2023
-
Abstract
- Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairwise relations and local neighborhoods defined by graph representations. In this paper we propose a novel TDL-based method for compressing signals over graphs, consisting in two main steps: first, disjoint sets of higher-order structures are inferred based on the original signal --by clustering $N$ datapoints into $K\ll N$ collections; then, a topological-inspired message passing gets a compressed representation of the signal within those multi-element sets. Our results show that our framework improves both standard GNN and feed-forward architectures in compressing temporal link-based signals from two real-word Internet Service Provider Networks' datasets --from $30\%$ up to $90\%$ better reconstruction errors across all evaluation scenarios--, suggesting that it better captures and exploits spatial and temporal correlations over the whole graph-based network structure.<br />Comment: Accepted as Oral at the Second Learning on Graphs Conference (LoG 2023). The recording of the talk can be found in https://www.youtube.com/watch?v=OcruIkiRkiU
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2308.11068
- Document Type :
- Working Paper