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..
Mining Heavy Temporal Subgraphs: Fast Algorithms and Applications
Authors: Jose Cadena, Anil Vullikanti
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithms in a diverse set of real and synthetic networks, and we ο¬nd solutions with higher score and better detection power for anomalous events compared to earlier heuristics. |
| Researcher Affiliation | Academia | Jose Cadena, Anil Vullikanti Department of Computer Science and Biocomplexity Institute, Virginia Tech, Blacksburg, VA 24061 EMAIL |
| Pseudocode | Yes | Algorithm 1 SELECTINTERVALS(t): Produce set of O(t log t) intervals.; Algorithm 2 FASTGW(H, w, Ο); Algorithm 3 FASTGW-HDS: Find a solution to HDS |
| Open Source Code | No | The paper links to a full version PDF, not source code. There is no explicit statement about releasing source code for the methodology. |
| Open Datasets | Yes | We evaluate our algorithms on ο¬ve real networks (see Table 2): (i) a Twitter follower graph with communication evolving over 6 months, (ii) the highway network of Los Angeles County, California and its activity on May, 20141, (iii) a contact network of health-care workers and patients in a hospital ward during 4 days (Vanhems et al. 2013), (iv) a sample of Wikipedia page view statistics, and (v) an autonomous systems network. ... 1http://pems.dot.ca.gov/ ... Ramakrishnan et al. 2014; Vanhems et al. 2013 |
| Dataset Splits | No | The paper mentions using real and synthetic networks but does not provide specific details on training, validation, or test dataset splits, percentages, or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes algorithms but does not mention any specific software components with version numbers (e.g., Python 3.x, PyTorch x.x, CPLEX x.x). |
| Experiment Setup | No | The paper discusses the experimental setup in terms of datasets and comparisons, but does not provide specific details on hyperparameters, training configurations, or system-level settings. |