Deep Homomorphism Networks
Authors: Takanori Maehara, Hoang NT
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments and observed that the DHN solved difficult benchmark problems (CSL, EXP, and SR25) with fewer parameters than the existing models. For real-world datasets, the proposed model showed promising results, but was still not competitive to the state-of-the-art models that involve a lot of engineering (see Section 6 for discussion). |
| Researcher Affiliation | Collaboration | Takanori Maehara* Roku, Inc. Cambridge, UK tmaehara@roku.com Hoang NT University of Tokyo Tokyo, Japan hoangnt@g.ecc.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1 Algorithm for tree pattern P. 1: procedure RECURSION(P , p) 2: dpprus Ð 0 for all u P V p Gq 3: for q P childrenppq do 4: dpq Ð RECURSIONp P , qq 5: dpprus Ð dpprus µppxuq ř v PNpuq dpqrvs for all u P V p Gq 6: end for 7: return dpp 8: end procedure |
| Open Source Code | Yes | 5The source code for DHN is provided at https://github.com/gear/dhn |
| Open Datasets | Yes | The Circular Skip Links (CSL) dataset consists of 150 undirected regular graphs of degree four [54]. EXP [1] and SR25 [2, 56] are datasets not distinguishable by 1-WL (EXP) and 3-WL (SR25). The ENZYMES [66, 8] and PROTEINS [8, 22] datasets represent the protein function prediction task formulated as the graph classification problem4 These datasets are parts of the TUDataset collection. |
| Dataset Splits | Yes | We report the stratified 10-fold cross-validation accuracies for ENZYMES and PROTEINS datasets in Table 1. |
| Hardware Specification | Yes | The reported results are obtained on a single GPU machine that houses an RTX4090 with 24GB of GPU memory. |
| Software Dependencies | No | The paper mentions "Pytorch Geometric s API" but does not specify its version or other software dependencies with version numbers. |
| Experiment Setup | Yes | For our DHN, we use two sets of patterns as the building blocks. Ci:j t Ci, . . . , Cju denotes the sets of cycles of lengths i to j. Similarly, Ki:j t Ki, . . . , Kju denotes the set of cliques of size i to j. We use 3-layer MLPs for both ρ and µp for the homomorphism layer (Eq. (4)). In Table 1, we present the models configurations inside the single brackets. ... All DHN models in Table 1 have 20 hidden units MLP layers; these MLP blocks (3 layers) correspond to functions µ in Equation 3. Each homomorphism kernel is embedded in 10 dimensions. The DHN models are trained using the Adam optimizer with an initial learning rate of 0.001. |