Results 171 to 180 of about 4,929,609 (209)

Solving inexact graph isomorphism problems using neural networks

Neurocomputing, 2005
We present a neural network approach to solve exact and inexact graph isomorphism problems for weighted graphs. In contrast to other neural heuristics or related methods this approach is based on a neural refinement procedure to reduce the search space followed by an energy-minimizing matching process. Experiments on random weighted graphs in the range
Fritz Wysotzki, Brijnesh J Jain
exaly   +2 more sources

A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network

IEEE Transactions on Cybernetics
Despite various measures across different engineering and social systems, network robustness remains crucial for resisting random faults and malicious attacks. In this study, robustness refers to the ability of a network to maintain its functionality after a part of the network has failed.
Lin Wang, Yang Lou, Chen Guanrong
exaly   +3 more sources

On neural networks for graph isomorphism problem

[Proceedings] 1992 RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, 1992
Although the Hopfield neural networks is known to provide an efficient algorithm for hard problems, it cannot always give the correct solution due to local minima. For the graph isomorphism problem (which has not yet been proved to be polynomially solvable or NP-complete), the authors first introduce a Hopfield network that shows a similar behavior ...
K. Agusa, S. Fujita, M. Yamashita, T. Ae
openaire   +1 more source

Influence of Krylov subspace in Graph Isomorphism for Mobile Networks

Mobile Networks and Applications, 2017
Identification of isomorphism among graphs is one of the computationally challenging tasks in computer science that couldn’t be solved in polynomial time. In this paper, we derive a polynomial time algorithm that allows direct comparison between different graph structures to check for graph isomorphism.
T. Ramraj, R. Prabhakar
openaire   +1 more source

Hypergraph Attention Isomorphism Network by Learning Line Graph Expansion

2020 IEEE International Conference on Big Data (Big Data), 2020
Graph neural networks (GNNs) are able to achieve state-of-the-art performance for node representation and classification in a network. But, most of the existing GNNs can be applied to simple graphs, where an edge connects only a pair of nodes. Studies have shown that hypergraphs are effective to model real-world relationships which are of higher order ...
Sambaran Bandyopadhyay, Kishalay Das, M. Narasimha Murty   +2 more
openaire   +2 more sources

Networks modulo graph isomorphism

2000
This chapter introduces the kernel of the network algebra, i.e., an algebraic theory of networks modulo graph isomorphism.
openaire   +1 more source

Graph isomorphisms effect on structure optimization of neural networks

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2003
Concepts from the graph theory and molecular evolution are proposed for analyzing effects of redundancy induced by graph isomorphisms on the structure optimization of neural networks. It is demonstrated that a graph database that considers isomorphisms can drastically reduce the number of evaluations in an evolutionary structure optimization process.
C. Igel, P. Stagge
openaire   +1 more source

Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023
Arunakumari B. N, Kshama S. B, Swasti S, Sameeksha B. H, Sonia Puri, Rubini P. A   +5 more
exaly   +3 more sources

Graph Isomorphism Network for Speech Emotion Recognition

Interspeech 2021, 2021
Jiawang Liu, Haoxiang Wang 0002
openaire   +2 more sources

A Unified Addressing Schema for Hexagonal and Honeycomb Networks with Isomorphic Cayley Graphs

First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06), 2006
As interconnection architectures, the regular six-degree hexagonal networks and the regular three-degree honeycomb networks have been widely investigated. For the lack of a proper addressing schema, some published routing algorithms are very complicated and the topological properties of some complex hexagonal and honeycomb related architectures are not
Mingxin He, Wenjun Xiao
openaire   +2 more sources