Dynamic Network Discovery via Infection Tracing
Authors: Ben Bals, Michelle Döring, Nicolas Klodt, George Skretas
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We round off our analysis with an experimental evaluation of our algorithm on real-world interaction data from the Stanford Network Analysis Project and on temporal Erd os-Renyi graphs.In Section 7, we empirically validate our theoretical results. Using both synthetic and real-world data, we execute the Discovery Follow algorithm and observe its performance. |
| Researcher Affiliation | Academia | Ben Bals1,2 , Michelle D oring3 , Nicolas Klodt3 and George Skretas3 1Centrum Wiskunde & Informatica, Amsterdam 2Vrije Universiteit Amsterdam 3Hasso Plattner Institute, Potsdam EMAIL, EMAIL, |
| Pseudocode | Yes | Algorithm 1: Follow discovers the neighbors of v0. Explore discovers their respective δ-ecc s. and Algorithm 2: The TGD extension of Follow. |
| Open Source Code | Yes | Both our implementation and analysis code are available under a permissive open-source license and can be used to replicate our findings.1 1See https://github.com/Ben Bals/dynamic-network-discovery. |
| Open Datasets | Yes | Secondly, to evaluate our algorithm on real-world data, we employ a data set from the Stanford Large Network Dataset Collection [Kumar et al., 2021]. The comm-f2f-Resistance collection is described by the project as a set of 62 dynamic face-to-face interaction network[s] between a group of participants . |
| Dataset Splits | No | No explicit dataset splits (training/test/validation) are mentioned. The paper describes generating synthetic temporal Erd os-Renyi graphs with parameters n, p, Tmax and using the entire comm-f2f-Resistance dataset for evaluation, rather than splitting it for typical machine learning tasks. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. It only states 'We run Discovery Follow on all graphs...'. |
| Software Dependencies | No | The paper states that implementation and analysis code are open-source but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions). |
| Experiment Setup | Yes | We test with 1 to 100 nodes in steps of size 5, for probabilities p {.01, .05, .1, .15, .2, .25, .3, .35, .4, .5, .7, .9}. We pick Tmax as a factor of n, testing with Tmax/n {.05, .1, .2, .3, .4, .5, .7, .9, 1, 2, 3, 5, 7, 10}. Similarly, δ is 1 or a multiple of Tmax, namely δ/Tmax {.01, .05, .1, .3, .5} We record the number of rounds needed to complete the discovery and how many of these rounds are spent in the component discovery versus the component exploration phases of the algorithm. |