{"ID":2879031,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16847","arxiv_id":"2508.16847","title":"Cyber Orbits of Large Scale Network Traffic","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.16847","url_pdf":"https://arxiv.org/pdf/2508.16847v1","authors":"[\"Jeremy Kepner\",\"Hayden Jananthan\",\"Chasen Milner\",\"Michael Houle\",\"Michael Jones\",\"Peter Michaleas\",\"Alex Pentland\"]","published":"2025-08-23T00:21:29Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cs.CR\",\"cs.NI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}