FUGAL: Feature-fortified Unrestricted Graph Alignment
Authors: Aditya Bommakanti, Harshith Vonteri, Konstantinos Skitsas, Sayan Ranu, Davide Mottin, Panagiotis Karras
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 adityabommakanti2002@gmail.com Harshith Reddy Vonteri IIT Delhi harshithreddyvonteri@gmail.com Konstantinos Skitsas Aarhus University skitsas@cs.au.dk Sayan Ranu IIT Delhi sayanranu@cse.iitd.ac.in Davide Mottin Aarhus University davide@cs.au.dk Panagiotis Karras University of Copenhagen & Aarhus University piekarras@gmail.com |
| 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. |