Tootfinder

fao_trade: FAO Multiplex Trade Network
Multiplex network representing trade relationships between countries from the Food and Agricultural Organization of the United Nations. Countries are nodes and edge types represent import/export of varying products. Data hosted by Manlio De Domenico.
This network has 214 nodes and 318346 edges.
Tags: Economic, Trade, Weighted, Multilayer

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Alexander D. Kalian, Lennart Otte, Jaewook Lee, Emilio Benfenati, Jean-Lou C. M. Dorne, Claire Potter, Olivia J. Osborne, Miao Guo, Christer Hogstrand
https://arxiv.org/abs/2507.17775

Comparison of Optimised Geometric Deep Learning Architectures, over Varying Toxicological Assay Data Environments
Geometric deep learning is an emerging technique in Artificial Intelligence (AI) driven cheminformatics, however the unique implications of different Graph Neural Network (GNN) architectures are poorly explored, for this space. This study compared performances of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs) and Graph Isomorphism Networks (GINs), applied to 7 different toxicological assay datasets of varying data abundance and endpoint, to perform binary classification of…

Autonomous driving using an optimized neural network based adaptive LPV-MPC controller
Yassine Kebbati, Naima Ait-Oufroukh, Vicen\c{c} Puig, Vincent Vigneron, Dalil Ichalal
https://arxiv.org/abs/2509.19523

Autonomous driving using an optimized neural network based adaptive LPV-MPC controller
Driverless vehicles are complex systems operating in constantly changing environments. Automated driving is achieved by controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control is one of the most promising tools for this type of application due to its optimal performance and ability to handle constraints. This paper addresses autonomous driving with an adaptive linear parameter varying model predictive controller (LPV-MPC), which is adapted by a neural network…

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
Joshua Daniel Loyal
https://arxiv.org/abs/2509.19748 https://arxiv.org/pdf/2509.19748

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However, two significant deficiencies exist in the dynamic random dot product graph literature: (1) no coherent Bayesian way to update one's prior beliefs about the latent positions in dynamic random dot product graphs due to their complicated constraints, and (2) no approach to forecast future networks with meaningful uncertainty quantification. This work proposes …

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ML-PWS: Estimating the Mutual Information Between Experimental Time Series Using Neural Networks
Manuel Reinhardt, Ga\v{s}per Tka\v{c}ik, Pieter Rein ten Wolde
https://arxiv.org/abs/2508.16509

ML-PWS: Estimating the Mutual Information Between Experimental Time Series Using Neural Networks
The ability to quantify information transmission is crucial for the analysis and design of natural and engineered systems. The information transmission rate is the fundamental measure for systems with time-varying signals, yet computing it is extremely challenging. In particular, the rate cannot be obtained directly from experimental time-series data without approximations, because of the high dimensionality of the signal trajectory space. Path Weight Sampling (PWS) is a computational technique…

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
Muhammad Z. Haq, Nadia N. Qadri, Omer Chughtai, Sadiq A. Ahmad, Waqas Khalid, Heejung Yu
https://arxiv.org/abs/2508.14471

Adaptive Network Selection for Latency-Aware V2X Systems under Varying Network and Vehicle Densities
This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet real-time network selection is hindered by diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs) such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to a…

Limited Improvement of Connectivity in Scale-Free Networks by Increasing the Power-Law Exponent
Yingzhou Mou, Yukio Hayashi
https://arxiv.org/abs/2509.17652 https://

Limited Improvement of Connectivity in Scale-Free Networks by Increasing the Power-Law Exponent
It has been well-known that many real networks are scale-free (SF) but extremely vulnerable against attacks. We investigate the robustness of connectivity and the lengths of the shortest loops in randomized SF networks with realistic exponents $2.0 < γ\leq 4.0$. We show that smaller variance of degree distributions leads to stronger robustness and longer average length of the shortest loops, which means the existing of large holes. These results will provide important insights toward enhancing…

Two-Timescale Dynamic Service Deployment and Task Scheduling with Spatiotemporal Collaboration in Mobile Edge Networks
Yang Li, Xing Zhang, Yunji Zhao, Wenbo Wang
https://arxiv.org/abs/2508.16293

Two-Timescale Dynamic Service Deployment and Task Scheduling with Spatiotemporal Collaboration in Mobile Edge Networks
Collaborative edge computing addresses the resource constraints of individual edge nodes by enabling resource sharing and task co-processing across multiple nodes. To fully leverage the advantages of collaborative edge computing, joint optimization of service deployment and task scheduling is necessary. Existing optimization methods insufficiently address the collaboration across spatial and temporal dimensions, which hinders their adaptability to the spatiotemporally varying nature of user dem…

fao_trade: FAO Multiplex Trade Network
Multiplex network representing trade relationships between countries from the Food and Agricultural Organization of the United Nations. Countries are nodes and edge types represent import/export of varying products. Data hosted by Manlio De Domenico.
This network has 214 nodes and 318346 edges.
Tags: Economic, Trade, Weighted, Multilayer