Lossy compression of weighted graph adjacency matrices by transform coding
For researchers working with large weighted graphs, this method offers a principled way to compress edge weights lossily, but it is incremental as it adapts transform coding to graphs via line graphs.
This paper proposes a lossy compression framework for weighted graphs where edge weights are compressed after transforming them into a graph signal on the line graph, achieving better compression than existing methods. Experiments on synthetic and real-world data validate the effectiveness.
In this paper, we propose a compression framework for weighted graphs in which the graph topology is transmitted losslessly and edge weights are compressed lossily. A challenge in the lossy compression of edge weights is that the underlying relationships between edges are ambiguous. To address this issue, we first transform the unweighted graph into the corresponding line graph, whose nodes represent the edges of the original graph and whose edges encode the relationships between them. The line graph transform allows us to regard edge weights as a graph signal defined on the line graph. Instead of transmitting the edge-weight vector, we first transform it with a graph filter bank on the line graph. Then, quantization and entropy coding are performed on the transformed coefficients of the edge weight vector. In addition to the lossy compression method, we formalize edge smoothness on the line graph and show that it serves as a measure of the difficulty of compression. The proposed smoothness measure can be easily calculated without converting to a line graph. This provides insight into the expected compression performance of a given weighted graph. Experiments on synthetic and real-world data validate the effectiveness of the proposed method by comparing it with existing matrix preprocessing methods.