Cyber Orbits of Large Scale Network Traffic

• URL: http://arxiv.org/abs/2508.16847v1
• Date: Sat, 23 Aug 2025 00:21:29 GMT
• Title: Cyber Orbits of Large Scale Network Traffic
• Abstract summary: Physical analogies for complicated network behavior can be a useful aid to understanding these newly discovered network phenomena.<n>The probability of observing a source at a temporal distance'' $r(t)$ at time $t$ is $p(t) propto 1/r(t)2$.<n>Applying this physical analogy to the observed source correlations in the Anonymized Network Sensing Graph Challenge data leads to an elegant cyber orbit analogy.
• Score: 4.556941519392727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of high-performance graph libraries, such as the GraphBLAS, has enabled the analysis of massive network data sets and revealed new models for their behavior. Physical analogies for complicated network behavior can be a useful aid to understanding these newly discovered network phenomena. Prior work leveraged the canonical Gull's Lighthouse problem and developed a computational heuristic for modeling large scale network traffic using this model. A general solution using this approach requires overcoming the essential mathematical singularities in the resulting differential equations. Further investigation reveals a simpler physical interpretation that alleviates the need for solving challenging differential equations. Specifically, that the probability of observing a source at a temporal ``distance'' $r(t)$ at time $t$ is $p(t) \propto 1/r(t)^2$. This analogy aligns with many physical phenomena and can be a rich source of intuition. Applying this physical analogy to the observed source correlations in the Anonymized Network Sensing Graph Challenge data leads to an elegant cyber orbit analogy that may assist with the understanding network behavior.

Related papers

• Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs [81.39865232674413]
  Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation.<n>This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs)
  arXiv  Detail & Related papers  (2026-06-02T15:42:14Z)
• Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions [7.2136602534376015]
  We investigate the interplay between data structure in feature learning for the most popular ICA algorithm, FastICA, and gradient descent (SGD)<n>We show that vanilla online SGD outperforms FastICA, and prove that the optimal sample complexity $ngtrsim d2$ can be reached by smoothing the loss.
  arXiv  Detail & Related papers  (2025-03-31T09:46:47Z)
• Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection [1.3673890873313355]
  Transformer-based models dominate NLP tasks like sentiment analysis, machine translation, and claim verification.<n>In this work, we explore Graph Neural Networks (GNNs) and Hyperbolic Graph Neural Networks (HGNNs) as lightweight yet effective alternatives for Environmental Claim Detection.
  arXiv  Detail & Related papers  (2025-02-19T11:04:59Z)
• Learning Mean Field Control on Sparse Graphs [28.313779052437134]
  We propose a novel mean field control model inspired by local weak convergence to include sparse graphs with coefficients above two.<n>Besides a theoretical analysis, we design scalable learning algorithms which apply to the challenging class of graph sequences with finite first moment.<n>As it turns out, our approach outperforms existing methods in many examples and on various networks due to the special design aiming at an important, but so far hard to solve class of MARL problems.
  arXiv  Detail & Related papers  (2025-01-28T17:03:30Z)
• Simulation of Graph Algorithms with Looped Transformers [6.0465914748433915]
  We study the ability of transformer networks to simulate algorithms on graphs from a theoretical perspective. We prove by construction that this architecture can simulate individual algorithms such as Dijkstra's shortest path. We show a Turing Completeness result with constant width when the extra attention heads are utilized.
  arXiv  Detail & Related papers  (2024-02-02T02:48:03Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity [0.0]
  This paper introduces a link prediction method designed explicitly for directed networks. It is based on the similarity-popularity paradigm, which has recently proven successful in undirected networks. The algorithms approximate the hidden similarities as shortest path distances using edge weights that capture and factor out the links' asymmetry and nodes' popularity.
  arXiv  Detail & Related papers  (2022-07-15T11:03:25Z)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• Graph network for simultaneous learning of forward and inverse physics [0.0]
  We propose an end-to-end graph network that learns forward and inverse models of particle-based physics using interpretable inductive biases. Our approach is able to predict the forward dynamics with at least an order of magnitude higher accuracy.
  arXiv  Detail & Related papers  (2021-12-13T22:38:09Z)
• Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions [50.674773358075015]
  We propose a simple graph-based network structure called GCHP, which utilizes only graph convolutional layers. We show that GCHP can significantly reduce training time and the likelihood ratio loss with interarrival time probability assumptions can greatly improve the model performance.
  arXiv  Detail & Related papers  (2021-07-07T16:59:14Z)
• Leveraging Sparse Linear Layers for Debuggable Deep Networks [86.94586860037049]
  We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. The resulting sparse explanations can help to identify spurious correlations, explain misclassifications, and diagnose model biases in vision and language tasks.
  arXiv  Detail & Related papers  (2021-05-11T08:15:25Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [79.81193813215872]
  We develop a hybrid (graph) neural network that combines a traditional graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. We show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions.
  arXiv  Detail & Related papers  (2020-07-08T21:23:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.