Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs
Authors: Nannan Wu, Feng Chen, Jianxin Li, Jinpeng Huai, Bo Li
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evidence demonstrates that our method is superior to stateof-the-art methods in several real-world anomaly detection tasks. |
| Researcher Affiliation | Academia | Dept. of Computer Science & Engineering, Beihang University, Beijing 100191, China Dept. of Computer Science, University at Albany, SUNY, Albany, NY 12203 |
| Pseudocode | Yes | Algorithm 1: Graph-TPP |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes using three datasets: "Respiratory Emergency Department (ED) Dataset" (simulated), "Water Pollution Data" (real-world network with simulated contaminant spread, citing a paper for the simulation method), and a "Real-World Network Dataset" (provided by an Internet company). None of these are explicitly stated as publicly available with access information (link, DOI, specific repository, or formal citation with authors/year) within the paper. |
| Dataset Splits | No | The paper describes the datasets and how noise was injected for "testing the robustness of methods to noise" (Section 5.1). However, it does not explicitly provide details about standard training, validation, or test dataset splits in terms of percentages, sample counts, or specific pre-defined splits for model development and evaluation in a typical machine learning context. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments, such as GPU/CPU models, memory, or cloud computing instance types. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | No | The paper describes the problem formulation, methodology, and evaluation metrics, but it does not provide concrete details about the experimental setup such as hyperparameters (e.g., learning rates, batch sizes), specific optimizer settings, or other system-level training configurations. |