# Lossy Compression of Adjacency Matrices by Graph Filter Banks

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)（2024）

摘要

This paper proposes a compression framework for adjacency matrices of
weighted graphs based on graph filter banks. Adjacency matrices are widely used
mathematical representations of graphs and are used in various applications in
signal processing, machine learning, and data mining. In many problems of
interest, these adjacency matrices can be large, so efficient compression
methods are crucial. In this paper, we propose a lossy compression of weighted
adjacency matrices, where the binary adjacency information is encoded
losslessly (so the topological information of the graph is preserved) while the
edge weights are compressed lossily. For the edge weight compression, the
target graph is converted into a line graph, whose nodes correspond to the
edges of the original graph, and where the original edge weights are regarded
as a graph signal on the line graph. We then transform the edge weights on the
line graph with a graph filter bank for sparse representation. Experiments on
synthetic data validate the effectiveness of the proposed method by comparing
it with existing lossy matrix compression methods.

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关键词

Graph signal processing,Graph Filter Banks,Matrix Compression,Line Graph

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