Uncovering Specific-Shape Graph Anomalies in Attributed Graphs
Authors: Nannan Wu, Wenjun Wang, Feng Chen, Jianxin Li, Bo Li, Jinpeng Huai5433-5440
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments Our experiments consist of two parts: (i) uncovering high quality specific shape attacking anomalies in the real-world edu.cn network dataset by our method; and (ii) demonstrating the efficiency of our method on different applications. |
| Researcher Affiliation | Academia | College of Intelligence and Computing, Tianjin University, Tianjin 300072, China Dept. of Computer Science, University at Albany, SUNY, Albany, NY 12203 Dept. of Computer Science & Engineering, Beihang University, Beijing 100191, China |
| Pseudocode | Yes | Algorithm 1: Query-map |
| Open Source Code | No | The paper does not provide a statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions several datasets (edu.cn network dataset, Water Pollution Dataset, Respiratory Emergency Department (ED) Dataset) and describes their collection or characteristics. However, it does not provide concrete access information (e.g., URL, DOI, specific citation to a public repository) for any of these datasets, nor does it refer to them as commonly available public datasets with standard citations. |
| Dataset Splits | No | The paper discusses the use of datasets for evaluation, but it does not specify explicit training, validation, or test splits (e.g., percentages or sample counts) for any of the datasets used in the experiments. |
| Hardware Specification | Yes | In Table 3, the running times were collected from the computer with Intel Xeon E3-1220 (e.g., 4 CPU, 3.1 GHz) and 24GB RAM. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or any specific library versions) that would be needed for reproducibility. |
| Experiment Setup | No | The paper describes the objective functions used (e.g., ϕEBP, ϕKULL) and mentions how baselines were tuned based on recommendations in their original papers (e.g., "k = 10, D = 2 to Topk (Gupta et al. 2014) and k = 20, d = 2 to Fast-k (Yang et al. 2016)"). However, it does not provide specific hyperparameters or system-level training settings for its own proposed method, Query-map. |