On the von Neumann entropy of graphs
The von Neumann entropy of a graph is a spectral complexity measure that has recently
found applications in complex networks analysis and pattern recognition. Two variants of the …
found applications in complex networks analysis and pattern recognition. Two variants of the …
Principle of relevant information for graph sparsification
Graph sparsification aims to reduce the number of edges of a graph while maintaining its
structural properties. In this paper, we propose the first general and effective information …
structural properties. In this paper, we propose the first general and effective information …
On the similarity between von Neumann graph entropy and structural information: Interpretation, computation, and applications
X Liu, L Fu, X Wang, C Zhou - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The von Neumann graph entropy is a measure of graph complexity based on the Laplacian
spectrum. It has recently found applications in various learning tasks driven by the …
spectrum. It has recently found applications in various learning tasks driven by the …
Network temperature: A novel statistical index for networks measurement and management
C Wang, X Li, E Bertino - ACM Transactions on Internet Technology …, 2022 - dl.acm.org
Being able to monitor each packet path is critical for effective measurement and
management of networks. However, such detailed monitoring can be very expensive …
management of networks. However, such detailed monitoring can be very expensive …
Bridging the gap between von Neumann graph entropy and structural information: Theory and applications
X Liu, L Fu, X Wang - Proceedings of the Web Conference 2021, 2021 - dl.acm.org
The von Neumann graph entropy (VNGE) is a measure of graph complexity based on the
Laplacian spectrum. It has recently found applications in various learning tasks driven by …
Laplacian spectrum. It has recently found applications in various learning tasks driven by …
The von Neumann Theil index: characterizing graph centralization using the von Neumann index
We show that the von Neumann entropy (from herein referred to as the von Neumann index)
of a graph's trace normalized combinatorial Laplacian provides structural information about …
of a graph's trace normalized combinatorial Laplacian provides structural information about …
[HTML][HTML] Symmetric laplacians, quantum density matrices and their von-neumann entropy
We show that the (normalized) symmetric Laplacian of a simple graph can be obtained from
the partial trace over a pure bipartite quantum state that resides in a bipartite Hilbert space …
the partial trace over a pure bipartite quantum state that resides in a bipartite Hilbert space …
The quantum Theil index: characterizing graph centralization using von Neumann entropy
We show that the von Neumann entropy (from herein referred to as the von Neumann index)
of a graph's trace normalized combinatorial Laplacian provides structural information about …
of a graph's trace normalized combinatorial Laplacian provides structural information about …
[HTML][HTML] On the von Neumann entropy of a graph
H Lin, B Zhou - Discrete Applied Mathematics, 2018 - Elsevier
The von Neumann entropy of a nonempty graph provides a mean of characterizing the
information content of the quantum state of a physical system. We give sharp upper and …
information content of the quantum state of a physical system. We give sharp upper and …
An Efficient Entropy-Based Graph Kernel
Graph kernels are methods used in machine learning algorithms for handling graph-
structured data. They are widely used for graph classification in various domains and are …
structured data. They are widely used for graph classification in various domains and are …