Fine-Grained Search Space Classification for Hard Enumeration Variants of Subset Problems
Authors: Juho Lauri, Sourav Dutta2314-2321
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the practicality of our approach on real-world networks with millions of vertices and edges by not only retaining all optimal solutions, but also aggressively pruning the input instance size resulting in several fold speedups of state-of-the-art algorithms. |
| Researcher Affiliation | Industry | Juho Lauri, Sourav Dutta Nokia Bell Labs, Ireland {juho.lauri, sourav.dutta}@nokia-bell-labs.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions using and implementing some existing tools, but does not state that its own framework's code is released. |
| Open Datasets | Yes | To demonstrate the practicality and usefulness of our framework, we use real-world networks from Network Repository (Rossi and Ahmed 2015) (http://networkrepository. com/). |
| Dataset Splits | Yes | A 4-fold cross validation over the 2178 feature vectors gives an average vertex classification accuracy of 0.96, the same over the 10746 feature vectors results in an average of 0.93, the same over the 2008 feature vectors results in an average of 0.97, and the same over the 2556 feature vectors results in an average of 0.80. |
| Hardware Specification | Yes | The experiments for real-world networks (Subsection 4.2) are executed on an Intel Core i7-4770K CPU (3.5 GHz), 8 GB of RAM, running Ubuntu 16.04. As an exception, we run experiments for cliquer on Intel Xeon E5-2680 and 102 GB of RAM. All experiments are done on an Intel Core i5-6300U CPU (2.4 GHz), 8 GB of RAM, running Ubuntu 16.04, differing only slightly from the earlier hardware configuration. |
| Software Dependencies | No | The paper mentions software packages like 'auto-sklearn (Feurer et al. 2015)', 'scikit-learn (Pedregosa et al. 2011)', 'igraph (Csardi and Nepusz 2006)', 'Em MCE (Cheng et al. 2011)', and 'cliquer (Niskanen and Osterg ard 2003)', but does not provide specific version numbers for these software dependencies, only citations to their originating papers/years. |
| Experiment Setup | Yes | We fix the confidence threshold q = 0.55. [...] Thus, we use a linear classifier (logistic regression) trained for 400 epochs with stochastic gradient descent. We use a standard L2 regularizer, and use 0.0001 as the regularization term multiplier determined by a grid search. We use the features described in Section 3. |