Tootfinder

Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro, Shirin Saeedi Bidokhti
https://arxiv.org/abs/2404.03227

Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks
We address the challenge of sampling and remote estimation for autoregressive Markovian processes in a multi-hop wireless network with statistically-identical agents. Agents cache the most recent samples from others and communicate over wireless collision channels governed by an underlying graph topology. Our goal is to minimize time-average estimation error and/or age of information with decentralized scalable sampling and transmission policies, considering both oblivious (where decision-makin…

Structural analysis and the sum of nodes' betweenness centrality in complex networks
Ronghao Deng, Meizhu Li, Qi Zhang
https://arxiv.org/abs/2402.13507

Structural analysis and the sum of nodes' betweenness centrality in complex networks
Structural analysis in network science is finding the information hidden from the topology structure of complex networks. Many methods have already been proposed in the research on the structural analysis of complex networks to find the different structural information of networks. In this work, the sum of nodes' betweenness centrality (SBC) is used as a new structural index to check how the structure of the complex networks changes in the process of the network's growth. We build two four diff…

This https://arxiv.org/abs/2312.03221 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Random walk models for the propagation of signalling molecules in one-dimensional spatial networks and their continuum limit
The propagation of signalling molecules within cellular networks is affected by network topology, but also by the spatial arrangement of cells in the networks. Understanding the collective reaction--diffusion behaviour in space of signals propagating through cellular networks is an important consideration for example for regenerative signals that convey positional information. In this work, we consider stochastic and deterministic versions of random walk models of signalling molecules propagati…

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Yankai Chen, Yixiang Fang, Qiongyan Wang, Xin Cao, Irwin King
https://arxiv.org/abs/2402.12411

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis. To deal with network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their full adaptive learning process may lead to insufficient information exploration, thereby formulating the problem as the isolated node value prediction with underperformance and less interpretabil…

Network shell structure based on hub and non-hub nodes
Gaogao Dong, Nannan Sun, Fan Wang, Renaud Lambiotte
https://arxiv.org/abs/2404.17231 https://…

Network shell structure based on hub and non-hub nodes
The shell structure holds significant importance in various domains such as information dissemination, supply chain management, and transportation. This study focuses on investigating the shell structure of hub and non-hub nodes, which play important roles in these domains. Our framework explores the topology of Erdös-Rényi (ER) and Scale-Free (SF) networks, considering source node selection strategies dependent on the nodes' degrees. We define the shell $l$ in a network as the set of nodes a…

Counting cherry reduction sequences is counting linear extensions (in phylogenetic tree-child networks)
Tom\'as M. Coronado, Joan Carles Pons, Gabriel Riera
https://arxiv.org/abs/2403.14491

Counting cherry reduction sequences is counting linear extensions (in phylogenetic tree-child networks)
Orchard and tree-child networks share an important property with phylogenetic trees: they can be completely reduced to a single node by iteratively deleting cherries and reticulated cherries. As it is the case with phylogenetic trees, the number of ways in which this can be done gives information about the topology of the network. Here, we show that the problem of computing this number in tree-child networks is akin to that of finding the number of linear extensions of the poset induced by each…

Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Yang Lu, Yuhang Li, Ruichen Zhang, Wei Chen, Bo Ai, Dusit Niyato
https://arxiv.org/abs/2404.11858

Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamen…

This https://arxiv.org/abs/2312.03221 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Random walk models for the propagation of signalling molecules in one-dimensional spatial networks and their continuum limit
The propagation of signalling molecules within cellular networks is affected by network topology, but also by the spatial arrangement of cells in the networks. Understanding the collective reaction--diffusion behaviour in space of signals propagating through cellular networks is an important consideration for example for regenerative signals that convey positional information. In this work, we consider stochastic and deterministic versions of random walk models of signalling molecules propagati…