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..
A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Authors: Feng Chen, Baojian Zhou
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compressive experiments to validate the effectiveness and efficiency of the proposed techniques. The proposed GRAPH-MP is applied to optimize a variety of graph scan statistic models for the task of connected subgraph detection. Extensive experiments demonstrate that GRAPH-MP performs superior over state-of-the-art methods that are customized for the task of connected subgraph detection on both running time and accuracy. |
| Researcher Affiliation | Academia | Feng Chen, Baojian Zhou Computer Science Department, University at Albany SUNY |
| Pseudocode | Yes | Algorithm 1 GRAPH-MP |
| Open Source Code | Yes | The implementation of GRAPH-MP is available at https://github.com/baojianzhou/Graph-MP. |
| Open Datasets | Yes | Datasets: 1) Water Pollution Dataset. The Battle of the Water Sensor Networks (BWSN) [Ostfeld et al., 2008] provides a real-world network... 2) High-Energy Physics Citation Network. The Cit Hep Ph (high energy physics phenomenology) citation graph is from the e-print ar Xiv... The data before 1999 is considered as training data, and the data from 1999 to 2002 is considered as testing data. |
| Dataset Splits | No | The paper mentions training and testing data splits but does not specify a separate validation split or cross-validation strategy. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify versions for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | We set k = 1000 by default... We tested the set of λ values: {0.02, 0.04, , 2.0}. Depth First Scan is an exact search algorithm and has an exponential time cost in the worst case scenario. We hence set a constraint on the depth of its search to 10 in order to reduce its time complexity. |