A Generalized Matching Pursuit Approach for Graph-Structured Sparsity

Authors: Feng Chen, Baojian Zhou

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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.