Results 81 to 90 of about 2,166 (199)

Topological Graph Neural Networks: A Novel Approach for Geometric Deep Learning

Applied AI Letters, Volume 7, Issue 2, June 2026.
This graphical abstract illustrates the Topological Graph Neural Network (TopGNN) architecture. It demonstrates a parallel processing approach where an input graph is simultaneously analyzed by a standard GNN Encoder to capture local node features and by Persistent Homology to extract global topological features (like cycles and voids), visualized as a
Amarjeet, Anurag Sinha, Khalid Hussain, Wasim Ahmad Bhat, Sudhanshu Maurya, Mohammad Nadeem Ahmed, Mohammad Rashid Hussain, S. Khalid   +7 more
wiley   +1 more source

Una red neuronal binaria para la identificación de mecanismos isomorfos. // A binary Neural network for identifying isomorphic mechanisms.

Ingeniería Mecánica, 2002
Un problema de importancia primordial en el diseño mecánico es identificar los mecanismos isomorfos, puesto que los isomorfismos nodetectados generan soluciones duplicadas y por tanto suponen un esfuerzo innecesario en el proceso de diseño.
G. Galán Marín, J. M. del Castillo Granados   +1 more
doaj  

Extending Undirected Graph Techniques to Directed Graphs via Category Theory

Mathematics
We use Category Theory to construct a ‘bridge’ relating directed graphs with undirected graphs, such that the notion of direction is preserved. Specifically, we provide an isomorphism between the category of simple directed graphs and a category we call ‘
Sebastian Pardo-Guerra, Vivek Kurien George, Vikash Morar, Joshua Roldan, Gabriel Alex Silva   +4 more
doaj   +1 more source

Distributed Testing of Graph Isomorphism in the CONGEST Model [PDF]

, 2020
In this paper we study the problem of testing graph isomorphism (GI) in the CONGEST distributed model. In this setting we test whether the distributive network, G_U, is isomorphic to G_K which is given as an input to all the nodes in the network, or ...
Levi, Reut, Medina, Moti
core   +1 more source

Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges

Biotechnology and Bioengineering, Volume 123, Issue 6, Page 1417-1431, June 2026.
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz, Anna‐Lena Drommershausen, Alexander Grünberger, Dirk Holtmann   +3 more
wiley   +1 more source

Predicting SARS‐CoV‐2 Infection With Graph Attention Capsule Networks

Computational Intelligence, Volume 42, Issue 3, June 2026.
ABSTRACT Recent studies in machine learning have demonstrated the effectiveness of applying graph neural networks (GNNs) to single‐cell RNA sequencing (scRNA‐seq) data to predict COVID‐19 disease states. In this study, we propose an explainable graph attention capsule network (GACapNet), which extracts and fuses Severe Acute Respiratory Syndrome ...
Runjie Zhu, Jimmy Xiangji Huang, Amran Bhuiyan, Zhiwen Xie, Guangyou Zhou   +4 more
wiley   +1 more source

Examining graph isomorphism using deep graph neural networks

Ovaj rad istražuje primjenu dubokih neuronskih mreža na problem testiranja izomorfizma grafova, fundamentalnog izazova u teoriji grafova s raznolikim primjenama.
Dautović, Anabel
core   +2 more sources

Extending Graph Pattern Matching with Regular Expressions

, 2020
Graph pattern matching, which is to compute the set M(Q, G) of matches of Q in G, for the given pattern graph Q and data graph G, has been increasingly used in emerging applications e.g., social network analysis.
Xin Wang, Xueyan Zhong, Wang, Yang, Xu, Yang, Yang Xu, Zhang, Ji, Wang, Xin, Yang Wang, Ji Zhang, Zhong, Xueyan   +9 more
core   +1 more source

Isomorphisms in Multilayer Networks

, 2017
We extend the concept of graph isomorphisms to multilayer networks with any number of "aspects" (i.e., types of layering). In developing this generalization, we identify multiple types of isomorphisms.
Mikko Kivela, Porter, Mason A., Kivela, Mikko, Mason A. Porter   +3 more
core   +1 more source

Beyond Weisfeiler–Lehman with Local Ego-Network Encodings

, 2023
Identifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbitrary
Vicenç Gómez, Kaltenbrunner, Andreas, Nurudin Alvarez-Gonzalez, Alvarez-Gonzalez, Nurudin, Gómez, Vicenç, Andreas Kaltenbrunner   +5 more
core   +1 more source