Hiding in Multilayer Networks
Authors: Marcin Waniek, Tomasz Michalak, Talal Rahwan1021-1028
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately. |
| Researcher Affiliation | Academia | 1Computer Science, New York University Abu Dhabi, 2Institute of Informatics, University of Warsaw |
| Pseudocode | Yes | Algorithm 1 All in one heuristic; Algorithm 2 Fringe heuristic; Algorithm 3 Density heuristic |
| Open Source Code | No | The paper does not include an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | No | The paper mentions general network models like "ER3(2000,10)", "WS3(2000,10)", "BA3(2000,5)" and refers to "real-life networks" in Waniek et al. (2019) for description, but it does not provide concrete access information (e.g., specific links, DOIs, or formal citations with author/year for dataset availability) for the datasets used in its simulations. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for key software components or libraries used. |
| Experiment Setup | Yes | The simulation process is as follows. For every network, we pick as potential evaders the nodes that are ranked among the top 10 according to at least one of the five considered centrality measures. We then simulate the hiding process for each one of those evaders separately. To this end, we choose the group of contacts to be the neighbors of the evader in the original network. After that, we remove all original edges between the evader and those contacts, and act as if the evader was never connected to those individuals, but rather wants to connect to them while remaining hidden from centrality analysis. Finally, we connect the evader to the contacts using edges chosen by one of our heuristics. We record the difference between the ranking of the evader in the original, unchanged network, and in the network after running the heuristic. |