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..
Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction
Authors: Changping Meng, S Chandra Mouli, Bruno Ribeiro, Jennifer Neville
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our proposed method significantly outperforms other stateof-the-art methods designed for static and/or single node/link prediction tasks. |
| Researcher Affiliation | Academia | Changping Meng, S Chandra Mouli, Bruno Ribeiro, Jennifer Neville Department of Computer Science Purdue University, West Lafayette, IN EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model architecture and steps in detail but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/PurdueMINDS/SPNN. |
| Open Datasets | Yes | We use two representative heterogeneous graph datasets with temporal information. DBLP (Sun et al. 2011b) contains scientific papers... Facebook is a sample of the Facebook users from one university. ... Word Net is a knowledge graph that groups words into synonyms... The WN18 dataset is a subset of Word Net... |
| Dataset Splits | Yes | 20% of the training examples are separated as validation for early stopping. |
| Hardware Specification | Yes | The server is an Intel E5 2.60GHz CPU with 512 GB of memory. |
| Software Dependencies | No | The paper states 'We implement SPNN in Theano' but does not provide version numbers for Theano or any other software dependencies. |
| Experiment Setup | Yes | The loss function is the negative log likelihood plus L1 and L2 regularization penalties over the parameters, both with regularization penalty 0.001. We train SPNN using stochastic gradient descent over a maximum of 30000 epochs and learning rate 0.01. |