A Graph Convolution Neural Network-Based Framework for Communication Network K-Terminal Reliability Estimation

Abstract

The exact computation of network k-terminal reliability is an NP-hard problem, and many approximation methods have been proposed as alternatives, among which the neural network-based approaches are believed to be the most effective and promising. However, the existing neural network-based methods either ignore the local structures in the network topology or process the local structures as Euclidean data, while the network topology represented by the graph is in fact non-Euclidean. Seeing that the Graph Convolution Neural network (GCN) is a generalization of convolution operators onto non-Euclidean data structure, in an effort to fill in the gap, this paper proposes a GCN-based framework for the estimation of communication network reliability. First, a dataset with sufficient sample size is constructed, by calculating the k-terminal reliability via the exact contraction-deletion method for the generated network samples. Then, an estimation model based on GCN is built, where several graph convolution layers process input information and extract node-level structural features from the network topology, a concatenation layer fuses the structural features into a graph-level representation feature, and a multi-layer perceptron computes the k-terminal reliability as output. To demonstrate the practicality and rationality of our proposed model, comparative experiments are carried out on 12 datasets, the results of which show that our proposed GCN model has an average of 59.60% and 57.52% improvement over existing methods on homogeneous datasets and heterogeneous datasets, respectively.

A network analysis-based framework to understand the representation dynamics of graph neural networks

“…To conclude, we point out that the expressive power of GNNs enabled a series of contexts and tasks to be successfully managed by them. In particular, researchers have investigated the usage of GNNs to solve problems in complex and social networks [39][40][41][42]. The ability of extracting node-level structural features from graphs is one of the key aspects of GNNs.…”

“…The ability of extracting node-level structural features from graphs is one of the key aspects of GNNs. For instance, in [39], the authors propose a GCN-based framework for the estimation of communication network reliability. This framework employs several graph convolution layers to extract nodelevel structural features from input information.…”