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].
FUGAL: Feature-fortified Unrestricted Graph Alignment
Authors: Aditya Bommakanti, Harshith Vonteri, Konstantinos Skitsas, Sayan Ranu, Davide Mottin, Panagiotis Karras
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation demonstrates that FUGAL consistently surpasses state-of-the-art graph alignment methods in accuracy across all benchmark datasets without encumbering efficiency. |
| Researcher Affiliation | Academia | Aditya Bommakanti IIT Delhi EMAIL Harshith Reddy Vonteri IIT Delhi EMAIL Konstantinos Skitsas Aarhus University EMAIL Sayan Ranu IIT Delhi EMAIL Davide Mottin Aarhus University EMAIL Panagiotis Karras University of Copenhagen & Aarhus University EMAIL |
| Pseudocode | Yes | Algorithm 1 FINDQUASIPERMUTATION (A, B, D, µ, T) ... Alg. 2 in the appendix presents the complete FUGAL pseudocode. |
| Open Source Code | Yes | 1Code and data at https://github.com/idea-iitd/Fugal. |
| Open Datasets | Yes | Table 1: Real-graph nodes n, edges m, and network type. ... Synthetic Graphs. We employ Newmann-Watts (NW) [21] graphs, characterized by small-world properties and a high clustering coefficient. |
| Dataset Splits | No | The paper discusses evaluation on noisy variants and different graph versions (e.g., 'align the last graph version to versions containing 80%, 85%, 90%, and 99% of edges') but does not explicitly provide traditional training/test/validation dataset splits (e.g., percentages or sample counts) needed to reproduce data partitioning for model training or selection. |
| Hardware Specification | Yes | We ran all experiments on a 40-core Intel Xeon E5-2687W CPU machine @3.10GHz with Python implementations of FUGAL1 and competitors; |
| Software Dependencies | No | The paper mentions 'Python implementations' of their method and competitors but does not provide specific version numbers for Python or any other key software libraries or dependencies. |
| Experiment Setup | Yes | Table 3 in Appendix lists the parameters we employ in FUGAL with each dataset. We set the number of iterations T to 15 for all datasets. The parameter µ controls the sway of node features in the optimization. ... We employ two noise types: one-way noise removes edges from the target graph, while bimodal noise removes and restores the same number of edges. |