4 papers
Lossy compression of weighted graph adjacency matrices by transform coding
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
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…
Multiscale Graph Construction Using Non-local Cluster Features
Reina Kaneko, Hayate Kojima, Kenta Yanagiya +3
This paper presents a multiscale graph construction method using both graph and signal features. Multiscale graph is a hierarchical representation of the graph, where a node at eac…
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness
Kenta Yanagiya, Koki Yamada, Yasuo Katsuhara +2
Finding important edges in a graph is a crucial problem for various research fields, such as network epidemics, signal processing, machine learning, and sensor networks. In this pa…
Lossy Compression of Adjacency Matrices by Graph Filter Banks
Kenta Yanagiya, Junya Hara, Hiroshi Higashi +2
This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations o…