Detecting Change Points in the Large-Scale Structure of Evolving Networks
Authors: Leto Peel, Aaron Clauset
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identiļ¬es a sequence of change points that align with known external shocks to these networks. |
| Researcher Affiliation | Academia | Leto Peel and Aaron Clauset Department of Computer Science University of Colorado Boulder, CO 80309 |
| Pseudocode | No | The paper describes the model and calculations mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for our method is available at http://tinyurl.com/letopeel/code.html |
| Open Datasets | Yes | We now apply these approaches to detect changes in two high-resolution evolving networks, the MIT Reality Mining proximity network (Eagle and Pentland 2006) and the Enron email network (Klimt and Yang 2004) |
| Dataset Splits | No | The paper mentions using synthetic data and real networks for testing, but it does not provide specific training/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers. |
| Experiment Setup | Yes | All tests used a w = 4 window size and a 0.05 false-positive rate. |