Directed graph contrastive learning
Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
Universal graph contrastive learning with a novel laplacian perturbation
Abstract Graph Contrastive Learning (GCL) is an effective method for discovering
meaningful patterns in graph data. By evaluating diverse augmentations of the graph, GCL …
meaningful patterns in graph data. By evaluating diverse augmentations of the graph, GCL …
Node importance ranking of complex networks with entropy variation
X Ai - Entropy, 2017 - mdpi.com
The heterogeneous nature of a complex network determines the roles of each node in the
network that are quite different. Mechanisms of complex networks such as spreading …
network that are quite different. Mechanisms of complex networks such as spreading …
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 …
A complex networks approach to find latent clusters of terrorist groups
Given the extreme heterogeneity of actors and groups participating in terrorist actions,
investigating and assessing their characteristics can be important to extract relevant …
investigating and assessing their characteristics can be important to extract relevant …
ESSEN: improving evolution state estimation for temporal networks using von neumann entropy
Q Huang, Y Zhang, Z Zhang… - Advances in Neural …, 2024 - proceedings.neurips.cc
Temporal networks are widely used as abstract graph representations for real-world
dynamic systems. Indeed, recognizing the network evolution states is crucial in …
dynamic systems. Indeed, recognizing the network evolution states is crucial in …
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 …
A graph entropy measure from urelement to higher-order graphlets for network analysis
Graph entropy measures have recently gained wide attention for identifying and
discriminating various networks in biology, society, transportation, etc. However, existing …
discriminating various networks in biology, society, transportation, etc. However, existing …
Spin statistics, partition functions and network entropy
This article explores the thermodynamic characterization of networks using the heat bath
analogy when the energy states are occupied under different spin statistics, specified by a …
analogy when the energy states are occupied under different spin statistics, specified by a …
Thermodynamic characterization of networks using graph polynomials
In this paper, we present a method for characterizing the evolution of time-varying complex
networks by adopting a thermodynamic representation of network structure computed from a …
networks by adopting a thermodynamic representation of network structure computed from a …