Titlebook: Complex Networks XV; Proceedings of the 1 Federico Botta,Mariana Macedo,Ronaldo Menezes Conference proceedings 2024 The Editor(s) (if appli

Exclaim

Kinematic-Based Force-Directed Graph Embedding,ow-dimensional space, graph embedding enables efficient graph processing and analysis tasks like node classification, link prediction, and visualization. In this paper, we propose and provide proof of convergence for a novel graph embedding paradigm where nodes are assumed to possess mass and a kine

pulse-pressure

,Deep Graph Machine Learning Models for?Epidemic Spread Prediction and?Prevention,ption of the appropriate safety measures and actions should also take into account other societal challenges, such as the impact on the local economy or the psychological strain on its inhabitants. Recent approaches for preventing an epidemic spread have led to the adoption of rather aggressive stra

TERRA

含鐵

violate

,Edge Dismantling with?Geometric Reinforcement Learning,maximize damage, is a known NP-hard problem. While heuristics for node removal exist, edge network dismantling, especially in real-world scenarios like power grids or transportation networks, remains underexplored. This paper introduces eGDM-RL, a novel framework for edge dismantling based on Geomet

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,Public Transit Inequality in?the?Context of?the?Built Environment,planners, alike, have stressed the importance of improving transit service for all. This entails ensuring that vulnerable demographics are not neglected as urban planning shifts towards more sustainable modes. Research in transport justice reveals the concentration of economically vulnerable individ

鬧劇

Conference proceedings 2024 or perhaps the reason is that it finds the perfect balance between young and senior participation, a balance in the demographics of the presenters, or perhaps it is just the quality of the work presented. .

受人支配

Theoretical and Experimental Principles,ing important entities, we evaluate the impact of relation extraction errors on the identification of central nodes in several real and synthetic networks. The impact of these errors varies significantly by graph topology, suggesting that confidence in measurements based on automatically extracted r

運(yùn)氣

LAVE

Theoretical and Experimental Principles,ted to semiconductors and minerals relevant to energy storage, namely lithium and graphite, both relevant sectors for the energy transition, and, employing our novel approach, we uncover recent trends in these markets.