# Retrieval Verification: Reviving_Life_on_the_Edge_Joint_Score_Based_Graph_Generation_of_Rich_Edge_Attributes_parsed

## Pilar: Background
**Query:** _Latar belakang, masalah, dan motivasi penelitian ini. Background, research problem, motivation, context, introduction, underlying issue._

### Rank 1 (Score: 0.3061)
**Header:** **C.3 Graph Benchmarks**
**Content:**
```text
Although we do not claim to have a superior distribution estimation for graphs where edge features are not necessary, and, in addition, our method is designed for directed graphs in contrast to all other methods, in  
Table 6: General graphs datasets evaluation. ’*’ means out of computation resources.  
|Method|Planar<br>deg_↓_<br>cl_↓_orb_↓_|SBM<br>deg_↓_cl_↓_orb_↓_|Ego Small<br>deg_↓_<br>cl_↓_<br>orb_↓_|Grid<br>deg_↓_<br>cl_↓_<br>orb_↓_|
|---|---|---|---|---|
||||||
|SPECTRE<br>GraphVAE<br>EDP-GNN<br>DiGress<br>GDSS|1_._42<br>1_._35 1_._33<br>0_._87<br>1_._13 0_._83<br>0_._985 1_._29 0_._97<br>1_._36<br>0_._97 1_._47<br>0_._945 0_._96 0_._66|2_._12 1_._37 0_._51<br>1_._41 0_._97 0_._52<br>1_._1<br>1_._43 0_._88<br>1_._16 1_._32 1_._16<br>0_._74 1_._57 0_._25|0_._046 0_._14<br>0_._73<br>0_._13<br>0_._23<br>0_._052<br>0_._062 0_._097 **0****_._009**<br>0_._12<br>0_._17<br>0_._035<br>0_._025 0_._087 0_._015|*<br>*<br>*<br>1_._48<br>**0**<br>0_._87<br>0_._45 0_._32 0_._51<br>0_._87 0_._03 1_._28<br>0_._37 0_._01 0_._42|
||||||
|Our w.o. edge features<br>Our|0_._032 0_._71 0_._34<br>**0****_._025 0****_._38 0****_._23**|0_._47<br>1_._1<br>0_._05<br>**0****_._46 0****_._63 0****_._04**|0_._02 0_._043 0_._052<br>**0****_._02 0****_._036** 0_._046|0_._07 0_._012 0_._45<br>**0****_._01** 0_._007 **0****_._39**|  
Table 7: Molecule QM9 dataset.  
|Method|Val w/o_↑_<br>Uni_↑_<br>FCD_↑_<br>NSPDK_↓_|
|---|---|
|||
```

### Rank 2 (Score: 0.2392)
**Header:** **References**
**Content:**
```text
- Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In _International conference on machine learning_ , pp. 2256–2265. PMLR, 2015.  
- Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. _Advances in neural information processing systems_ , 32, 2019.  
- Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In _9th International Conference on Learning Representations, ICLR_ , 2021.  
- Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun. Scenegen: Learning to generate realistic traffic scenes. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pp. 892–901, 2021.  
- Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone, and Philipp Kraehenbuehl. Language conditioned traffic generation. _arXiv preprint arXiv:2307.07947_ , 2023.  
- Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. Digress: Discrete denoising diffusion for graph generation. In _The Eleventh International Conference on Learning Representations, ICLR_ , 2023.
```

### Rank 3 (Score: 0.2255)
**Header:** **B.4.3 SwinGNN-E Implementation Details**
**Content:**
```text
We adapt SwinGNN (Yan et al., 2023) to the edge-important benchmarks. We construct similar changes as we did for GDSS-E regarding directed graphs, and for multiple edge features, we modify the one-hot encoding configuration to work with the original data instead. We follow the same training protocols Yan et al. (2023) implemented for other similar datasets. We report the results in the main text.
```

## Pilar: Methodology
**Query:** _Metode, pendekatan, arsitektur, algoritma, eksperimen, atau dataset yang digunakan. Methodology, proposed method, approach, system architecture, dataset, materials, experimental setup, procedure._

### Rank 1 (Score: 0.4815)
**Header:** **B.2.4 General Datasets**
**Content:**
```text
We present the datasets below and refer to Vignac et al. (2023) and Jo et al. (2022) for more information about the evaluation process and protocols.  
- **Ego-small** - 200 small ego graphs drawn from larger Citeseer network dataset (Sen et al., 2008).  
- **Grid** - 100 standard 2D grid graphs. BRENDA database (Schomburg et al., 2004).  
- **Stochastic-Block-Model (SBM)** - This dataset comprises 200 synthetic stochastic block model graphs. These graphs have communities ranging from two to five, with each community containing between 20 to 40 nodes. The probability of inter-community edges is 0.3, and intra-community edges is 0.05. Validity is determined based on the number of communities, the number of nodes in each community, and a statistical test as done in Martinkus et al. (2022).  
- **Planar** - This dataset comprises 200 synthetic planar graphs, each containing 64 nodes. The criteria for a valid graph within this dataset necessitate a two-fold condition: 1) the graph is connected, ensuring that every node has a path to every other node, and 2) the graph must exhibit planarity, meaning you can draw it on a two-dimensional plane without any edge crossings.
```

### Rank 2 (Score: 0.4563)
**Header:** **B.2.1 MDP Grid Maze - Datasets**
**Content:**
```text
**Dataset generator.** We will provide the complete code for generating the grids and their corresponding MDPs. The generator enables control of grid size, number of ending points, and number of blocks. In addition, if these parameters are valid, the code generates only valid graphs with a start and an end. Finally, the generator gets the desired grids and randomly samples grids with the above parameters.  
**Dataset statistics.** First, our grids are **5** _×_ **5** . Second, there is only one ending point. Therefore, we have **one** starting cell and **one** ending cell. Finally, we set the number of blocks to be precisely **4** . These configurations are for every grid in the dataset. We generate 1000 valid grids and split them into 80% training and 20% for testing and validations.
```

### Rank 3 (Score: 0.4326)
**Header:** **B.4.1 Architecture and Hyperparameters**
**Content:**
```text
We refer to Sec. 4 in the main text to describe the method architecture and implementation details. We present the hyperparameters per dataset in Tab. 4. We used Adam (Kingma & Ba, 2014) optimizer and the same learning rate of 0 _._ 01, weight decay of 0 _._ 0001, and EMA of 0 _._ 999 for all datasets. In addition, for sampling, we used the Euler predictor, Langevin corrector, signal-to-noise-ratio (SNR) of 0 _._ 05, scale epsilon of 0 _._ 7, and sampling with 1000 steps for all datasets. Finally, we use a single VP forward diffusion process (Song et al., 2021) stochastic differential equation for both the nodes and the edges. We refer to Jo et al.  
Table 4: Hyperparameters for each dataset.  
||Hyperparameter MDP MDP-non-det nuScense QM9 Planar SBM Ego Small Grid|
|---|---|
|||
|Module|Attention layers<br>5<br>5<br>3<br>3<br>4<br>4<br>5<br>4<br>Edges channels<br>4<br>2<br>1<br>2<br>2<br>2<br>2<br>2<br>Initial channels<br>1<br>1<br>1<br>1<br>1<br>1<br>1<br>1<br>Hidden channels<br>8<br>8<br>8<br>8<br>8<br>8<br>8<br>8<br>Final channels<br>4<br>4<br>4<br>4<br>4<br>4<br>4<br>4<br>Attention Heads<br>4<br>4<br>4<br>4<br>4<br>4<br>4<br>4<br>Hidden dimension<br>32<br>32<br>32<br>16<br>32<br>32<br>32<br>32|
|||
|Training|Batch size<br>256<br>256<br>128<br>1024<br>64<br>26<br>128<br>7<br>Epochs<br>5000<br>5000<br>5000<br>400<br>5000<br>5000<br>5000<br>5000|
|||
|SDE|_βmin_<br>0.1<br>0.1<br>0.1<br>0.1<br>0.1<br>0.1<br>0.1<br>0.1<br>_βmax_<br>3<br>3<br>2<br>1<br>1<br>1<br>1<br>1|
```

## Pilar: Gap
**Query:** _Gap penelitian, keterbatasan, kelemahan, atau masalah yang belum terselesaikan. Research gap, limitations, weakness, unresolved problem, remains a challenge, yet to be explored, however._

### Rank 1 (Score: 0.3825)
**Header:** **B.3 Limitations**
**Content:**
```text
Our approach exhibits superior performance compared to other methods on the MDP and nuScence datasets. However, there remains a quality disparity between the generated and original graphs. For example, in the MDP dataset, the generated graphs occasionally fail to adhere to the original constraints of start and end cells. We anticipate that an effective generative method would autonomously learn and adhere to such constraints. We believe that incorporating inductive bias regarding specific tasks can contribute to those tasks. Moreover, while score-based and diffusion methods typically demonstrate optimal performance across various downstream tasks in graph generation, scaling these methods to very large graphs containing millions of nodes and edges presents a challenge yet to be fully addressed.
```

### Rank 2 (Score: 0.3094)
**Header:** **1 Introduction**
**Content:**
```text
Generative modeling is a persistent challenge in scientific and engineering fields, with broad practical use cases. The primary goal is to understand a large database’s inherent distribution, enabling new samples to be generated. In many use cases, using a graph representation is convenient for describing the samples, such as in molecule and protein design (Du et al., 2022), neural architecture search (Oloulade et al., 2021), program synthesis (Gulwani et al., 2017), and more (Zhu et al., 2022).  
The exploration of generative modeling is a longstanding endeavor, marked by the development of various methodologies throughout the years, including variational autoencoders (Kingma & Welling, 2014), adversarial learning (Goodfellow et al., 2014), normalizing flows (Rezende & Mohamed, 2015), and diffusion models (Sohl-Dickstein et al., 2015). These approaches have been used to generate various information types  
(Dhariwal & Nichol, 2021; Naiman et al., 2024b;a) and solve many downstream tasks (Ho et al., 2022; Naiman et al., 2023; Berman et al., 2024). Recently, the generation of graph data has gained increased attention (Li et al., 2018; You et al., 2018). In particular, the modeling of graph distributions through score-based approaches (Song & Ermon, 2019) stands out as a promising avenue that necessitates a deeper investigation.
```

### Rank 3 (Score: 0.3054)
**Header:** **5.1 Synthetic Dataset Ablation Experiment**
**Content:**
```text
Train Data
GDSS-E
Ours  
**----- Start of picture text -----**<br>
Train Data GDSS-E Ours<br>**----- End of picture text -----**<br>  
![](/tmp/tmp4b1sto0h/Reviving_Life_on_the_Edge_Joint_Score-Based_Graph_Generation_of_Rich_Edge_Attributes.pdf-0009-02.png)  
![](/tmp/tmp4b1sto0h/Reviving_Life_on_the_Edge_Joint_Score-Based_Graph_Generation_of_Rich_Edge_Attributes.pdf-0009-03.png)  
![](/tmp/tmp4b1sto0h/Reviving_Life_on_the_Edge_Joint_Score-Based_Graph_Generation_of_Rich_Edge_Attributes.pdf-0009-04.png)  
Figure 4: A qualitative comparison between the original data (left), GDSS-E (middle), and our method (right) on the deterministic MDP grid maze dataset. Blocks are colored in blue (squares), and start and finish nodes in yellow (diamonds) and green (triangles), respectively. Our graphs consistently have four blocks, one start node, and one finish node, as required.  
homogeneous aspects, but it is challenged by the data distribution. We conclude that our new components are important: 1) the joint SDE process is crucial in modeling intra-edge interactions, and 2) the GNM module is instrumental in capturing inter-edge relationships.
```

## Pilar: Objective
**Query:** _Tujuan utama, sasaran, atau pertanyaan penelitian yang ingin dijawab. Main objective, research goal, purpose of this study, aims to investigate, to evaluate, this paper proposes._

### Rank 1 (Score: 0.3076)
**Header:** **B.2.3 Molecule Datasets**
**Content:**
```text
**QM9** - The Quantum Mechanics 9 database (Wu et al., 2018) contains around 130k small organic molecules with up to 9 heavy atoms and their physical properties in equilibrium, computed using density functional theory calculations. To evaluate our and other methods, we repeat the protocol presented in Vignac et al. (2023); Jo et al. (2022) and refer to them to learn more about the metrics and the evaluation protocols.
```

### Rank 2 (Score: 0.2798)
**Header:** **B.4.3 SwinGNN-E Implementation Details**
**Content:**
```text
We adapt SwinGNN (Yan et al., 2023) to the edge-important benchmarks. We construct similar changes as we did for GDSS-E regarding directed graphs, and for multiple edge features, we modify the one-hot encoding configuration to work with the original data instead. We follow the same training protocols Yan et al. (2023) implemented for other similar datasets. We report the results in the main text.
```

### Rank 3 (Score: 0.2543)
**Header:** **B.2.4 General Datasets**
**Content:**
```text
We present the datasets below and refer to Vignac et al. (2023) and Jo et al. (2022) for more information about the evaluation process and protocols.  
- **Ego-small** - 200 small ego graphs drawn from larger Citeseer network dataset (Sen et al., 2008).  
- **Grid** - 100 standard 2D grid graphs. BRENDA database (Schomburg et al., 2004).  
- **Stochastic-Block-Model (SBM)** - This dataset comprises 200 synthetic stochastic block model graphs. These graphs have communities ranging from two to five, with each community containing between 20 to 40 nodes. The probability of inter-community edges is 0.3, and intra-community edges is 0.05. Validity is determined based on the number of communities, the number of nodes in each community, and a statistical test as done in Martinkus et al. (2022).  
- **Planar** - This dataset comprises 200 synthetic planar graphs, each containing 64 nodes. The criteria for a valid graph within this dataset necessitate a two-fold condition: 1) the graph is connected, ensuring that every node has a path to every other node, and 2) the graph must exhibit planarity, meaning you can draw it on a two-dimensional plane without any edge crossings.
```

## Pilar: Futurework
**Query:** _Pekerjaan di masa depan, saran untuk peneliti lain, atau langkah selanjutnya. Future work, recommendations, future research directions, next steps, further investigation._

### Rank 1 (Score: 0.3206)
**Header:** **Eitan Kosman**
**Content:**
```text
_Bosch_  
**Dotan Di Castro** _Bosch_  
**Omri Azencot** _Ben-Gurion University_  
**Reviewed on OpenReview:** _`https: // openreview. net/ forum? id= pxdSm7PW5Q`_
```

### Rank 2 (Score: 0.2798)
**Header:** **B.4.3 SwinGNN-E Implementation Details**
**Content:**
```text
We adapt SwinGNN (Yan et al., 2023) to the edge-important benchmarks. We construct similar changes as we did for GDSS-E regarding directed graphs, and for multiple edge features, we modify the one-hot encoding configuration to work with the original data instead. We follow the same training protocols Yan et al. (2023) implemented for other similar datasets. We report the results in the main text.
```

### Rank 3 (Score: 0.2654)
**Header:** **B.2.4 General Datasets**
**Content:**
```text
We present the datasets below and refer to Vignac et al. (2023) and Jo et al. (2022) for more information about the evaluation process and protocols.  
- **Ego-small** - 200 small ego graphs drawn from larger Citeseer network dataset (Sen et al., 2008).  
- **Grid** - 100 standard 2D grid graphs. BRENDA database (Schomburg et al., 2004).  
- **Stochastic-Block-Model (SBM)** - This dataset comprises 200 synthetic stochastic block model graphs. These graphs have communities ranging from two to five, with each community containing between 20 to 40 nodes. The probability of inter-community edges is 0.3, and intra-community edges is 0.05. Validity is determined based on the number of communities, the number of nodes in each community, and a statistical test as done in Martinkus et al. (2022).  
- **Planar** - This dataset comprises 200 synthetic planar graphs, each containing 64 nodes. The criteria for a valid graph within this dataset necessitate a two-fold condition: 1) the graph is connected, ensuring that every node has a path to every other node, and 2) the graph must exhibit planarity, meaning you can draw it on a two-dimensional plane without any edge crossings.
```
