Tootfinder

Optimizing the Network Topology of a Linear Reservoir Computer
Sahand Tangerami, Nicholas A. Mecholsky, Francesco Sorrentino
https://arxiv.org/abs/2509.23391 https://

Optimizing the Network Topology of a Linear Reservoir Computer
Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform complex tasks. Reservoir computing is a machine learning tool that leverages high-dimensional dynamical systems to efficiently process temporal data for prediction and observation tasks. Traditionally, the connectivity of a reservoir computer (RC) is generated at random, lacking a principled design. Here, we focus on optimizing the topology of a linear RC to …

gnutella: Gnutella p2p networks (2002)
A sequence of 9 snapshots of the Gnutella peer-to-peer file sharing network from 5-31 August 2002. Nodes are hosts in the Gnutella network topology and edges are connections between them.
This network has 8717 nodes and 31525 edges.
Tags: Technological, Peer-to-peer, Unweighted
https://

Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks
S M Mustaquim, Asim K. Dey, Abhijit Mandal
https://arxiv.org/abs/2509.23450

Understanding How Network Geometry Influences Diffusion Processes in Complex Networks: A Focus on Cryptocurrency Blockchains and Critical Infrastructure Networks
This study provides essential insights into how diffusion processes unfold in complex networks, with a focus on cryptocurrency blockchains and infrastructure networks. The structural properties of these networks, such as hub-dominated, heavy-tailed topology, network motifs, and node centrality, significantly influence diffusion speed and reach. Using epidemic diffusion models, specifically the Kertesz threshold model and the Susceptible-Infected (SI) model, we analyze key factors affecting diff…

🏆 Counting down to the European Digital Connectivity Awards 2025!
Next week, the winners will be announced—and we’re honoured that GÉANT’s GN4-3N project is a finalist in the “Cross-border and international connectivity” category.
This recognition reflects the collaborative effort of the EU, GÉANT and our community of European NRENs to build a more inclusive, terabit-ready, and fully sovereign network infrastructure for Europe’s

Rebuild AC Power Flow Models with Graph Attention Networks
Yuting Hu, Jinjun Xiong
https://arxiv.org/abs/2509.22733 https://arxiv.org/pdf/2509.22733…

Rebuild AC Power Flow Models with Graph Attention Networks
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be con…

Griffiths phase emerging from strong mutualistic disorder in high-dimensional interacting systems
Tommaso Jack Leonardi, Amos Maritan, Sandro Azaele
https://arxiv.org/abs/2509.24692

Griffiths phase emerging from strong mutualistic disorder in high-dimensional interacting systems
The majority of analysis of interacting systems is done for weak and well-balanced interactions, when in fact topology and rare event factors often result in strong and sign-biased interactions when considering real systems. We analyse the impact of strong mutualistic interactions in a uniformly weighted Erdős-Rényi network under generalised Lotka-Volterra dynamics. In difference to the typical case we show the interaction topology and system dynamics combine to produce power law abundance di…

internet_top_pop: Internet topology (PoP level) (1969-2012)
Assorted snapshots of internet graph at the Point of Presence (PoP) level (which lies between the IP and AS levels), collected from around the world and at various times. The earliest snapshots are for ARPANET (1969-1972), with a few more from pre-2000. Most are from 2006 onward. Metadata include link type or speed, longitudes, and latitudes of nodes, URL, and date of record.
This network has 13 nodes and 17 edges.

AMHRP: Adaptive Multi-Hop Routing Protocol to Improve Network Lifetime for Multi-Hop Wireless Body Area Network
Muhammad Mateen Yaqoob, Kulsoom Fatima, Shahab Shamshirband, Amir Mosavi, Waqar Khurshid
https://arxiv.org/abs/2510.12698

AMHRP: Adaptive Multi-Hop Routing Protocol to Improve Network Lifetime for Multi-Hop Wireless Body Area Network
This paper presents a protocol for enhancement of life time of WBAN network as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

gnutella: Gnutella p2p networks (2002)
A sequence of 9 snapshots of the Gnutella peer-to-peer file sharing network from 5-31 August 2002. Nodes are hosts in the Gnutella network topology and edges are connections between them.
This network has 26518 nodes and 65369 edges.
Tags: Technological, Peer-to-peer, Unweighted
https://

TopROI: A topology-informed network approach for tissue partitioning
Sergio Serrano de Haro Iv\'a\~nez, Joshua W. Moore, Lucile Grzesiak, Eoghan J. Mullholand, Heather Harrington, Simon J. Leedham, Helen M. Byrne
https://arxiv.org/abs/2510.12772

TopROI: A topology-informed network approach for tissue partitioning
Mammalian tissue architecture is central to biological function, and its disruption is a hallmark of disease. Medical imaging techniques can generate large point cloud datasets that capture changes in the cellular composition of such tissues with disease progression. However, regions of interest (ROIs) are usually defined by quadrat-based methods that ignore intrinsic structure and risk fragmenting meaningful features. Here, we introduce TopROI, a topology-informed, network-based method for par…

internet_top_pop: Internet topology (PoP level) (1969-2012)
Assorted snapshots of internet graph at the Point of Presence (PoP) level (which lies between the IP and AS levels), collected from around the world and at various times. The earliest snapshots are for ARPANET (1969-1972), with a few more from pre-2000. Most are from 2006 onward. Metadata include link type or speed, longitudes, and latitudes of nodes, URL, and date of record.
This network has 24 nodes and 37 edges.

Network topological reorganization mechanisms of primary visual cortex under multimodal stimulation (in mice) https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1678035/full

Frontiers | Network topological reorganization mechanisms of primary visual cortex under multimodal stimulation
IntroductionThe functional connectivity topology of the primary visual cortex (V1) shapes sensory processing and cross-modal integration, yet how different s...

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati, Eilam Gross
https://arxiv.org/abs/2510.08162

TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely on a single event topology, restricting their applicability to realistic analyses where multiple signal and background processes with different structures are present. To overcome this, we present TIGER, a novel hierarchical graph network that is fundamentally…

Interplay of choice and topology in percolation on mediation-driven attachment networks
Nilomber Roy, M. M. B. Sheraj, M. K. Hassan
https://arxiv.org/abs/2510.10076 https://

Interplay of choice and topology in percolation on mediation-driven attachment networks
We investigate bond percolation on mediation-driven attachment (MDA) networks under the generalized Achlioptas process, where $M>1$ candidate bonds are sampled and the one that minimizes the resulting cluster size is selected the best-of-$M$ rule. This framework offers a systematic approach to investigate how network topology and choice mechanisms jointly shape percolation behavior. We analyze the effects of the degree exponent $ω$ and the choice parameter $M$ on the critical point $t_c$ and t…

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Xinren Zhang, Sixi Cheng, Zixin Zhong, Jiadong Yu
https://arxiv.org/abs/2510.07888 https:…

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Multi-agent systems (MAS) solve complex problems through coordinated autonomous entities with individual decision-making capabilities. While Multi-Agent Reinforcement Learning (MARL) enables these agents to learn intelligent strategies, it faces challenges of non-stationarity and partial observability. Communications among agents offer a solution, but questions remain about its optimal structure and evaluation. This paper explores two underexamined aspects: communication topology and informatio…

Less is More: Strategic Expert Selection Outperforms Ensemble Complexity in Traffic Forecasting
Walid Guettala, Yufan Zhao, L\'aszl\'o Guly\'as
https://arxiv.org/abs/2510.07426

Less is More: Strategic Expert Selection Outperforms Ensemble Complexity in Traffic Forecasting
Traffic forecasting is fundamental to intelligent transportation systems, enabling congestion mitigation and emission reduction in increasingly complex urban environments. While recent graph neural network approaches have advanced spatial temporal modeling, existing mixture of experts frameworks like Time Enhanced Spatio Temporal Attention Model (TESTAM) lack explicit incorporation of physical road network topology, limiting their spatial capabilities. We present TESTAM+, an enhanced spatio tem…

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu, Peter Steenkiste, Srinivasan Seshan
https://arxiv.org/abs/2510.03891

Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are typically described using shapes. There is an inherent tension between minimizing network contention and maximizing cluster utilization when placing various-shaped jobs. While existing schedulers typically optimize for one objective at the expense of the other, we demonstrate that both can be achieved simultaneously. …

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Cristian F. Jim\'enez-Var\'on, Marina I. Knight
https://arxiv.org/abs/2510.06157

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Patterns of dependence in financial networks, such as global bank connectedness, evolve over time and across frequencies. Analysing these systems requires statistical tools that jointly capture temporal dynamics and the underlying network topology. This work develops a novel spectral analysis framework for Generalized Network Autoregressive (GNAR) processes, modeling dependencies beyond direct neighbours by incorporating r-stage neighbourhood effects, unlike existing methods that at best rely s…

Emergent Network of Josephson Junctions in a Kagome Superconductor
Tycho J. Blom, Matthijs Rog, Marieke Altena, Andrea Capa Salinas, Stephen D. Wilson, Chuan Li, Kaveh Lahabi
https://arxiv.org/abs/2510.09777

Emergent Network of Josephson Junctions in a Kagome Superconductor
Materials with a Kagome lattice are intensely studied because they host novel, exotic states that combine strong correlations and electronic topology. The AV3Sb5 (A = K, Rb, Cs) group, in particular, is of major interest due to its combination of charge density waves, unconventional superconductivity, and indications of time-reversal symmetry breaking and electronic nematicity. Recently, critical current oscillations in an external magnetic field were observed in an unstructured flake of CsV3Sb…

Conformalized Gaussian processes for online uncertainty quantification over graphs
Jinwen Xu, Qin Lu, Georgios B. Giannakis
https://arxiv.org/abs/2510.06181 https://

Conformalized Gaussian processes for online uncertainty quantification over graphs
Uncertainty quantification (UQ) over graphs arises in a number of safety-critical applications in network science. The Gaussian process (GP), as a classical Bayesian framework for UQ, has been developed to handle graph-structured data by devising topology-aware kernel functions. However, such GP-based approaches are limited not only by the prohibitive computational complexity, but also the strict modeling assumptions that might yield poor coverage, especially with labels arriving on the fly. To…

Homogeneous Coupled Cell Systems with High-dimensional Internal Dynamics
S\"oren von der Gracht, Eddie Nijholt, Bob Rink
https://arxiv.org/abs/2510.06740 https://

Homogeneous Coupled Cell Systems with High-dimensional Internal Dynamics
The analysis of network dynamics is oftentimes restricted to networks with one-dimensional internal dynamics. Here, we show how symmetry explains the relation between behavior of systems with one-dimensional internal dynamics and with higher dimensional internal dynamics, when the network topology is the same. Fundamental networks of homogeneous coupled cell systems (B. Rink, J. Sanders. Coupled Cell Networks and Their Hidden Symmetries. SIAM J. Math. Anal. 46.2 (2014)) can be expressed in term…

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

gnutella: Gnutella p2p networks (2002)
A sequence of 9 snapshots of the Gnutella peer-to-peer file sharing network from 5-31 August 2002. Nodes are hosts in the Gnutella network topology and edges are connections between them.
This network has 10879 nodes and 39994 edges.
Tags: Technological, Peer-to-peer, Unweighted
https://

DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
Laurent Brisson (IMT Atlantique - DSD), C\'ecile Bothorel (IMT Atlantique - DSD), Nicolas Duminy (IMT Atlantique, IMT Atlantique - DSD)
https://arxiv.org/abs/2510.06245

DynBenchmark: Customizable Ground Truths to Benchmark Community Detection and Tracking in Temporal Networks
Graph models help understand network dynamics and evolution. Creating graphs with controlled topology and embedded partitions is a common strategy for evaluating community detection algorithms. However, existing benchmarks often overlook the need to track the evolution of communities in real-world networks. To address this, a new community-centered model is proposed to generate customizable evolving community structures where communities can grow, shrink, merge, split, appear or disappear. This…

Perceived Fairness in Networks
Arthur Charpentier
https://arxiv.org/abs/2510.12028 https://arxiv.org/pdf/2510.12028…

Perceived Fairness in Networks
The usual definitions of algorithmic fairness focus on population-level statistics, such as demographic parity or equal opportunity. However, in many social or economic contexts, fairness is not perceived globally, but locally, through an individual's peer network and comparisons. We propose a theoretical model of perceived fairness networks, in which each individual's sense of discrimination depends on the local topology of interactions. We show that even if a decision rule satisfies standard …

Why is topology hard to learn?
D. O. Oriekhov, Stan Bergkamp, Guliuxin Jin, Juan Daniel Torres Luna, Badr Zouggari, Sibren van der Meer, Naoual El Yazidi, Eliska Greplova
https://arxiv.org/abs/2509.26261

Why is topology hard to learn?
Much attention has been devoted to the use of machine learning to approximate physical concepts. Yet, due to challenges in interpretability of machine learning techniques, the question of what physics machine learning models are able to learn remains open. Here we bridge the concept a physical quantity and its machine learning approximation in the context of the original application of neural networks in physics: topological phase classification. We construct a hybrid tensor-neural network obje…

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Davide Rucci, Emanuele Carlini, Patrizio Dazzi, Hanna Kavalionak, Matteo Mordacchini
https://arxiv.org/abs/2510.00758

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate a…

A Flux-Tunable Discrete Angular Filter
Tristan M. Lawrie, Oliver M. Brown
https://arxiv.org/abs/2510.06395 https://arxiv.org/pdf/2510.06395

A Flux-Tunable Discrete Angular Filter
Recent work by Lawrie et al. [PRR 7, 023209 (2025)] introduced a non-diffracting resonant angular filter on a network of thin channels (modelled via quantum graph theory) that exhibits unit transmission of acoustic waves at a discrete, symmetry-paired set of incidence angles determined solely by the graph topology, while transmission at all other angles is strictly forbidden. In the present work, we study the same filtering geometry for waves governed by the magnetic Schrödinger equation rathe…

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

gnutella: Gnutella p2p networks (2002)
A sequence of 9 snapshots of the Gnutella peer-to-peer file sharing network from 5-31 August 2002. Nodes are hosts in the Gnutella network topology and edges are connections between them.
This network has 26518 nodes and 65369 edges.
Tags: Technological, Peer-to-peer, Unweighted
https://

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

topology: Internet AS graph (2004)
An integrated snapshot of the structure of the Internet at the level of Autonomous Systems (ASs), reconstructed from multiple sources, including the RouteViews and RIPE BGP trace collectors, route servers, looking glasses, and the Internet Routing Registry databases. This snapshot was created around October 2004.
This network has 34761 nodes and 171403 edges.
Tags: Technological, Communication, Unweighted, Multigraph, Timestamps

internet_top_pop: Internet topology (PoP level) (1969-2012)
Assorted snapshots of internet graph at the Point of Presence (PoP) level (which lies between the IP and AS levels), collected from around the world and at various times. The earliest snapshots are for ARPANET (1969-1972), with a few more from pre-2000. Most are from 2006 onward. Metadata include link type or speed, longitudes, and latitudes of nodes, URL, and date of record.
This network has 22 nodes and 25 edges.