arXiv (All)
open access
Spectral Dynamics of DeepWalk Embeddings for Dynamic Network Change-Point Detection
Dynamic networks describe evolving relational systems in which abrupt structural changes may signal anomalous events or important transitions. Detecting such changes requires distinguishing genuine structural signals from fluctuations in network observations and learned representations. We propose a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Fluid dynamics reduction methods for temporal networks.#295269
- Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering#1132074
- Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift#346528
- Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance#924259
- Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance#920028
Record · ID 1011608
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive