… Perceptron (MLP)– properly designed for compression as well. Obtained results clearly
suggest that our topological framework defines the best baseline for lossy neural compression. …

… The compressed representation introduced in this article is motivated by a classical result
of elementary algebraic topology. Simply and intuitively, a two-manifold is a surface such that …

… [5], GraphSAGE [9]) to perform signal compression leveraging the network physical topology; …
MPNN: a custom graph-based MPNN scheme –over the original physical network topology …

… of topology; the lesson is that nearness can be studied implicitly and local signals can be …
Detectors summarize and extract the information from a signal, leaving a highly compressed …

… Yet, selecting a proper compression method is challenging as there exist a … in compressing
graphs. To facilitate this, we present a survey and taxonomy on lossless graph compression …

… an intrinsic graph connectivity or graph topology. The … the graph signal in the spectral domain,
which is beneficial for geometric data processing such as reconstruction and compression. …

… Nevertheless, in practice, GNNs highly rely on graph topology, as … Is it possible, and if so,
how can we encode graph topology as a … knowledge even without full graph topology as input? …

… a graph topology from the data. In this article, we survey solutions to the problem of … graph
signal model that we consider goes beyond the global smoothness of the signal on the graph …

… Assuming the original signal is “smooth” with respect to the network topology, our approach
… respect to the graph Laplacian eigenbasis, leveraging ideas from compressed sensing. We …

… DCT has a broad spectrum of discrete signal processing (DSP) applications, predominantly
in signal compression as shown in [1]. The energy concentration, as well as the properties of …