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 [1].
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. |