# Retrieval Verification: Reviving_Life_on_the_Edge_Joint_Score_Based_Graph_Generation_of_Rich_Edge_Attributes_parsed

## Pilar: Background
**Query:** _latar belakang masalah, urgensi penelitian, motivasi mendasar, dilatar belakangi oleh, fenomena krusial, konteks situasi saat ini, dan alasan utama studi ini dilakukan._

### Rank 1 (Score: 0.8401)
**Header:** **5 Experiments**
**Content:**
```text
We tested our qualitative and quantitative methods on diverse real-world and synthetic dataset benchmarks. The objective of the model is to learn from observed graphs the underlying distribution and be able to generate new unseen graphs that follow the same distribution. We refer to App. B.1 for a detailed problem specification. The particular objectives of this study are:  
- As our main focus is edge features generation, we show in Sec. 5.1 that incremental modifications of GDSS are ineffective, highlighting the non-trivial importance of our approach.  
- We introduce a new challenging synthetic dataset of Markov decision processes (Sec. 5.2). Further, we present a new use case: a real-world traffic generation task (Sec. 5.3). To the best of our knowledge, we are the first to tackle this task via graph generation.  
- We evaluate the score estimation quality (Sec. 5.4), and we conclude by ablating our model to analyze the contribution of every component to its performance (Sec. 5.5).
```

### Rank 2 (Score: 0.8388)
**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: 0.8324)
**Header:** 2. _Nodes_
**Content:**
```text
- (a) Valid Solution (VS): Measures if a generated grid is valid: has start and finish cells, and the route is not entirely blocked.  
- (b) Blocks (B): Measures the absolute distance between the average number of blocks in the grids. In this work, the used dataset has a ground-truth average of four. For example, if a generated graph contains seven blocks, the distance will be 3, as the original distribution has only 4 blocks. The model is expected to match this target distribution.  
- (c) Start and Finish (SF): Measures the absolute distance between the average number of end and start cells; there are always exactly two in the setup we used. For example, if a generated graph contains two starting cells and two finish cells, the distance will be 2, as the original distribution has only one for each, and the model is expected to match this target distribution.  
- (d) Empty (E): Measures the absolute distance between the average number of regular cells. In this work, the dataset has a ground truth of 19 empty cells, and, similar to the previous two metrics, the generated graph should match this number.
```

## Pilar: Methodology
**Query:** _rincian teknis metodologi, arsitektur sistem, desain eksperimen, teknik pengumpulan dataset, algoritma yang diusulkan, sampel, dan prosedur pengujian._

### Rank 1 (Score: 0.8319)
**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 2 (Score: 0.8291)
**Header:** **1 Introduction**
**Content:**
```text
The closest evidence to graph generation with attributes appears in “Attributed Graph Generation” (Pfeiffer III et al., 2014; Lemaire et al., 2024). However, this formulation only associates attributes with the nodes, leaving the edges free from additional attributes besides the adjacency matrix. As we find critical tasks where edge attributes are dominant, we want to promote awareness of this problem to enable the generation of graphs with richer information and more expressiveness than the current formulation of attributed graphs. Consequently, in this study, we leverage the evident insight that edge attributes convey neighborhood information and provide instrumental data absent in the adjacency matrix crucial for generating edge attributes. We propose to encode graph distributions via a joint Stochastic Differential Equation (SDE), describing the evolution of node and edge attributes. Importantly, our technique jointly solves for graph elements. Consequently, it benefits from the synergetic connections between nodes and edges. In comparison, GDSS (Jo et al., 2022) proposed a similar diffusion system for adjacency and nodes that opt for a separated solution that may be sub-optimal in encoding certain graphs. We solve our joint SDE by further strengthening the dependencies between nodes and edges. In practice, this is achieved by combining node, edge, and adjacency information in an attention module, maximizing the mutual interplay of the graph components. Overall, our
```

### Rank 3 (Score: 0.8247)
**Header:** **5 Experiments**
**Content:**
```text
We tested our qualitative and quantitative methods on diverse real-world and synthetic dataset benchmarks. The objective of the model is to learn from observed graphs the underlying distribution and be able to generate new unseen graphs that follow the same distribution. We refer to App. B.1 for a detailed problem specification. The particular objectives of this study are:  
- As our main focus is edge features generation, we show in Sec. 5.1 that incremental modifications of GDSS are ineffective, highlighting the non-trivial importance of our approach.  
- We introduce a new challenging synthetic dataset of Markov decision processes (Sec. 5.2). Further, we present a new use case: a real-world traffic generation task (Sec. 5.3). To the best of our knowledge, we are the first to tackle this task via graph generation.  
- We evaluate the score estimation quality (Sec. 5.4), and we conclude by ablating our model to analyze the contribution of every component to its performance (Sec. 5.5).
```

## Pilar: Gap
**Query:** _keterbatasan penelitian terdahulu, kelemahan metode sebelumnya, kekosongan literatur, masalah yang belum terselesaikan, namun demikian, akan tetapi, dan hasil yang belum jelas._

### Rank 1 (Score: 0.8332)
**Header:** **5.4 Comparison of Score Losses**
**Content:**
```text
In this experiment, we compare the behavior of graph generation models in terms of the node and edge score losses on the train and test sets. We report these losses throughout training on the nuScenes dataset in Fig. 5. The left column corresponds to the train set, and the right to the test set. The top row shows the node losses, whereas the bottom row shows the edge losses. We use blue and orange for the loss measures of our method and GDSS-E, respectively. While the node score losses are comparable for both models, our method yields significantly better edge losses.
```

### Rank 2 (Score: 0.8307)
**Header:** **C.5 Standard Deviation in Experiments**
**Content:**
```text
We present the results of the quantitative evaluations with standard deviation to emphasize our method’s robustness. We show the MDP deterministic setting in Tab. 9. We show the MDP non-deterministic setting in Tab. 10. In Tab. 11, we show nuScenes. In Tab. 12, we show the general graphs experiment with standard deviation.  
Table 9: Deterministic MDPs with standard deviation.  



Table 10: Non-deterministic MDPs with standard deviation.
```

### Rank 3 (Score: 0.8286)
**Header:** **5.2 Generative MDPs**
**Content:**
```text
In this context, we introduce a new synthetic MDP dataset of grid mazes. Each grid has 5 _×_ 5 cells (nodes), including a start cell and a finish cell and 25 cells in total. There are also block cells that the agent cannot traverse, while the remaining cells are empty and walkable. The agents take one action per cell {up, left, down, right}. The maze is encoded via a graph whose nodes are the cells, and edges are the optional transitions. The nodes in the graph, each with a single feature, represent the discrete category of each cell {start, finish, block, empty}. The attributes on the edges are the continuous probabilities of moving from the current cell to one of its adjacent cells. Here, we consider two settings of using this data: (1) a deterministic grid maze, where edge features are binary in _{_ 0 _,_ 1 _}_ ; and (2) a non-deterministic grid maze, where edge attributes are probabilities in [0 _,_ 1], and the sum of all features per cell is one. Further details on these datasets and their MDP graphs are provided in App. B.2.1.  
A unique characteristic of our MDP graphs is their multiple attributes per edge. To the best of our knowledge, this scenario has yet to be studied in existing generative works. In particular, prior works are designed  
Table 1: Quantitative graph generation metrics on deterministic and non-deterministic grid mazes.
```

## Pilar: Objective
**Query:** _tujuan utama penelitian, sasaran spesifik, kontribusi inti makalah, hasil yang ingin dicapai, mengeksplorasi, dan solusi yang diusulkan untuk menyelesaikan masalah._

### Rank 1 (Score: 0.8441)
**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 2 (Score: 0.8296)
**Header:** **2 Related Work**
**Content:**
```text
**Score-based generative models.** Diffusion and score-based models represent generative models that sample new data by iteratively denoising a simple, pre-defined distribution (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al., 2020). Song et al. (2021) showed that these methods can be described in a unified framework of SDEs. Thus, we will use the terms diffusion and score-based models interchangeably. The diffusion process consists of the forward pass, where noise is gradually added to the data until it converges to a normal Gaussian distribution, and the reverse pass, where a backward SDE is integrated using a denoising model. New samples are defined as the convergence points of the reverse pass. While several graph generative frameworks exist (Wu et al., 2020; Zhou et al., 2020), we focus on score-based approaches.
```

### Rank 3 (Score: 0.8243)
**Header:** **5.1 Synthetic Dataset Ablation Experiment**
**Content:**
```text
In this section, we conduct an in-depth study to assess the performance gains from incorporating our different components. Our goal is to demonstrate that the current baseline, even with incremental adjustments for edge feature generation, is ineffective for this task, whereas our approach effectively learns both inter- and intra-edge feature attributes as intended.  
**Dataset.** We utilize a synthetic dataset of 1000 complete graphs with ten nodes each, with only edge attributes. The nodes do not contain any information. Let _E_ be the set of edges in a sampled graph. Each _e ∈ E_ belongs to R[2] and the two features in _e_ are sampled randomly from _only one_ of the Gaussian clusters depicted in Fig. 3a. Additionally, the graph edges are homogeneous, i.e., they are all sampled from the same cluster (the upper right or the lower left).
```

## Pilar: Futurework
**Query:** _saran untuk penelitian selanjutnya, rekomendasi pekerjaan di masa depan, arah pengembangan masa depan, dan peluang studi berikutnya untuk mengatasi batasan saat ini._

### Rank 1 (Score: 0.8355)
**Header:** **2 Related Work**
**Content:**
```text
**Score-based generative models.** Diffusion and score-based models represent generative models that sample new data by iteratively denoising a simple, pre-defined distribution (Sohl-Dickstein et al., 2015; Song & Ermon, 2019; Ho et al., 2020). Song et al. (2021) showed that these methods can be described in a unified framework of SDEs. Thus, we will use the terms diffusion and score-based models interchangeably. The diffusion process consists of the forward pass, where noise is gradually added to the data until it converges to a normal Gaussian distribution, and the reverse pass, where a backward SDE is integrated using a denoising model. New samples are defined as the convergence points of the reverse pass. While several graph generative frameworks exist (Wu et al., 2020; Zhou et al., 2020), we focus on score-based approaches.
```

### Rank 2 (Score: 0.8310)
**Header:** **C.5 Standard Deviation in Experiments**
**Content:**
```text
We present the results of the quantitative evaluations with standard deviation to emphasize our method’s robustness. We show the MDP deterministic setting in Tab. 9. We show the MDP non-deterministic setting in Tab. 10. In Tab. 11, we show nuScenes. In Tab. 12, we show the general graphs experiment with standard deviation.  
Table 9: Deterministic MDPs with standard deviation.  



Table 10: Non-deterministic MDPs with standard deviation.
```

### Rank 3 (Score: 0.8273)
**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.
```
