Results 101 to 110 of about 2,166 (199)
Simplifying large-scale communication networks with weights and cycles
PhDA communication network is a complex network designed to transfer information from a source to a destination. One of the most important property in a communication network is the existence of alternative routes between a source and destination node.
Liu, Ling
core
Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search
Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.
Chiu, Alvin +5 more
core
In this paper, we propose a Graph Neural Network (GNN)-based link prediction model to analyze the interconnections of Drug-related Organized Crime Groups (DOCG) on X (formerly Twitter) and to identify their potential associations.
Eun-Young Park +2 more
doaj +1 more source
To understand the cellular functions of genes requires investigating a variety of biological data, including experimental data, annotation from online databases and literatures, information about cellular interactions, and domain knowledge from ...
Li, Jie
core +1 more source
XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics
In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model comprises a botnet detector and an explainer for automatic forensics.
Lo, Wai Weng +4 more
core
Towards Faster Matrix Diagonalization with Graph Isomorphism Networks and the AlphaZero Framework
In this paper, we introduce innovative approaches for accelerating the Jacobi method for matrix diagonalization, specifically through the formulation of large matrix diagonalization as a Semi-Markov Decision Process and small matrix diagonalization as a Markov Decision Process.
Geigh Zollicoffer +6 more
openaire +2 more sources
Edge-Centric Federated Subgraph Isomorphism Counting via Residual Graph Neural Networks
Subgraph isomorphism counting is a fundamental yet computationally challenging task in graph analysis, with broad applications in bioinformatics and social network mining.
Jianjun Shi, Qinglong Wu, Xinming Zhang
doaj +1 more source
Graph Random Neural Features for Distance-Preserving Graph Representations [PDF]
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding naturally deals with graph isomorphism and preserves the metric structure of the
L. Livi, C. Alippi, D. Zambon
core
Surgical scheduling is a complex, stochastic, and dynamic task in hospital resource planning. Maximizing the use of limited surgical resources and improving the efficiency of general surgery departments remain key challenges in healthcare operations ...
Yixin Tong +4 more
doaj +1 more source
SICGNN: structurally informed convolutional graph neural networks for protein classification
Recently, graph neural networks (GNNs) have been widely used in various domains, including social networks, recommender systems, protein classification, molecular property prediction, and genetic networks.
YongHyun Lee +3 more
doaj +1 more source

