HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Authors: Geon Lee, Minyoung Choe, Kijung Shin
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We review our experiments to answer Q1-Q4: Accuracy. In Transaction, we use score U and set α = 0.98, K = 4, and M = 350. In Semi U and Semi B, we use score U and score B, respectively, and commonly set α = 0.98, K = 15, and M = 20. As discussed later, these summaries take up less space than the original hypergraphs. As shown in Figure 3, HASHNWALK accurately detects anomalous hyperedges in real and semi-real hypergraphs. |
| Researcher Affiliation | Academia | Geon Lee , Minyoung Choe and Kijung Shin Kim Jaechul Graduate School of AI, KAIST, Seoul, South Korea {geonlee0325, minyoung.choe, kijungs}@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 HASHNWALK Input: (1) hyperedge stream: E = {(ei, ti)} i=1, (2) number of supernodes M, (3) number of hash functions K, (4) time-decaying parameter α Output: stream of anomaly scores {yi} i=1 1: S RM M and T RM Initialize to zeros 2: for each hyperedge (ei, ti) E do 3: m(ei) summarize ei via hashing Sect. 4.1 4: update S and T Sect. 4.2 5: yi (score U(ei), score B(ei)) Sect. 4.3 6: end for 7: return {yi} i=1 |
| Open Source Code | Yes | Reproducibility: The source code and datasets are available at https://github.com/geonlee0325/Hash NWalk. |
| Open Datasets | Yes | Reproducibility: The source code and datasets are available at https://github.com/geonlee0325/Hash NWalk. |
| Dataset Splits | No | The paper describes an online anomaly detection setting and evaluation on streams from a specific `tsetup` point, but does not specify explicit train/validation/test dataset splits with percentages or counts as is common in supervised learning contexts. |
| Hardware Specification | Yes | Machines. We ran F-FADE on a workstation with an Intel Xeon 4210 CPU, 256GB RAM, and RTX2080Ti GPUs. We ran the others on a desktop with an Intel Core i9-10900KF CPU and 64GB RAM. |
| Software Dependencies | No | The paper states: “We implemented HASHNWALK and LSH in C++ and Python, respectively. For the others, we used the official open-source implementation.” However, it does not specify version numbers for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | In Transaction, we use score U and set α = 0.98, K = 4, and M = 350. In Semi U and Semi B, we use score U and score B, respectively, and commonly set α = 0.98, K = 15, and M = 20. |