Computing Optimal Monitoring Strategy for Detecting Terrorist Plots
Authors: Zhen Wang, Yue Yin, Bo An
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments showing that our algorithm can obtain a robust enough solution outperforming widely-used centrality based heuristics significantly and scale up to realistic-sized problems. |
| Researcher Affiliation | Academia | Zhen Wang School of Computer Engineering Nanyang Technological University Singapore 639798 wangzhen@ntu.edu.sg Yue Yin The Key Lab of Intelligent Information Processing, ICT, CAS University of Chinese Academy of Sciences Beijing 100190, China melody1235813@gmail.com Bo An School of Computer Engineering Nanyang Technological University Singapore 639798 boan@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: DO-TPD overview; Algorithm 2: better O-D (x, y); Algorithm 3: better O-A (x, y); Algorithm 4: Local Search (v, A, x) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on three types of graph structures which are widely used to model terrorist organisations: (i) Random trees (RT), where every new vertex is attached to a randomly picked incumbent; (ii) Erd os-R enyi random graphs (ER(V , M)), where exactly M edges are randomly constructed between all the possible pairs of vertices (Erd os and R enyi 1959); (iii) Barab asi-Albert scale-free networks (BA(k)), where each new vertex is connected to k incumbents using a preferential attachment mechanism (Barab asi and Albert 1999). We conduct experiments on two 9/11 networks (a small one with 19 vertices, who are directly responsible for this attack, and a bigger one with 37 vertices including accomplices (Krebs 2002)) |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits. The problem is formulated as a game, and the algorithms find optimal strategies, rather than training a model on data splits. |
| Hardware Specification | Yes | All computations are performed on a machine with a 3.20GHz quad core CPU and 16.00GB memory. |
| Software Dependencies | Yes | All LPs and MILPs are solved with CPLEX (version 12.6). |
| Experiment Setup | Yes | The parameters in better O-A (Algorithm 3), tmax, cmax and kmax are set to |V |, 0.2 |V | and 3, respectively. The number of resources R is set to |V | / 5 unless otherwise specified. The capability of each vertex τv is randomly chosen in [1, 5], and the network externality measure δ is fixed to be 0.1. |