Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams
Authors: Geon Lee, Minyoung Choe, Kijung Shin
IJCAI 2022 | Venue PDF | 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 EMAIL |
| 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. |