# Retrieval Verification: Reviving_Life_on_the_Edge_Joint_Score_Based_Graph_Generation_of_Rich_Edge_Attributes_parsed

## Pilar: Background
**Query:** _Fenomena ini merupakan isu krusial. Oleh karena itu, studi ini dilakukan untuk merespons urgensi permasalahan yang melatarbelakangi penelitian._

### Rank 1 (Score: 0.8131)
**Header:** **5.3 nuScenes: traffic scene generation**
**Content:**
```text
Table 2: Quantitative metrics on nuScenes.  
Learning traffic scenes can hugely contribute to autonomous driving. To the best of our knowledge, we are the first to suggest a generation of traffic scenes as graphs. We leverage the idea that a scene with elements such as cars, tracks, traffic lights, lanes, and  
|Method|deg_↓_cl_↓_un_↑_no_↑_V_↓_<br>O_↓_<br>L_↓_CR_↓_LA_↓_|
|---|---|
|||
|GDSS-E<br>SG-E<br>Our|1_._05 0_._03 15 34 3_._9 **0****_._66**0_._96 0_._5% 208<br>0_._79 0_._01 39 42 0_._76 1_._27 1_._08 0_._6% 302<br>**0****_._776****_e−_7 51 51 0****_._36** 0_._8 **0****_._080****_._3**%**194**|
```

### Rank 2 (Score: 0.8112)
**Header:** **1 Introduction**
**Content:**
```text
Recent works on score-based models for graph generation have made significant strides but remain limited in scope (Niu et al., 2020; Jo et al., 2022; Fan et al., 2023). While proficient in their designated tasks, these models either completely exclude edge attributes or treat graph components separately, limiting their capacity to capture complex relational structures. We address these limitations with a unified framework that enables the generation of node and edge attributes. An exemplary task with dominant edge attributes is shown in Fig. 1. This task involves translating a driving scene into a graph representation, similar to the approach in VectorNet (Gao et al., 2020). One prominent feature is the existence of edge attributes that introduce relative and interactional information between the road participants. For instance, an edge feature like a “looking at” flag–which indicates if one vehicle is actively observing another–captures situational awareness crucial in dense traffic. This relational cue, such as mutual acknowledgment during lane changes, is necessary for accurately modeling driving behavior but emerges only from the interaction itself, making it impossible to derive from node features alone. This and less expressive versions of such representation are prevalent in motion prediction literature (Huang et al., 2022), with models utilizing it often leading the prediction task leaderboards (Caesar et al., 2020). Consequently, we are interested in generating
```

### Rank 3 (Score: 0.8083)
**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.
```

## Pilar: Methodology
**Query:** _Penelitian ini menggunakan pendekatan terstruktur di mana data dikumpulkan dan analisis dilakukan menggunakan teknik serta metode yang dijelaskan pada bagian ini._

### Rank 1 (Score: 0.8422)
**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.
```

### Rank 2 (Score: 0.8392)
**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: 0.8384)
**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: Gap
**Query:** _Meskipun banyak studi telah dilakukan, masih terdapat kekosongan literatur (gap), kelemahan, dan batasan pada penelitian sebelumnya yang belum dieksplorasi._

### Rank 1 (Score: 0.8037)
**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: 0.8032)
**Header:** **5.3 nuScenes: traffic scene generation**
**Content:**
```text
Table 2: Quantitative metrics on nuScenes.  
Learning traffic scenes can hugely contribute to autonomous driving. To the best of our knowledge, we are the first to suggest a generation of traffic scenes as graphs. We leverage the idea that a scene with elements such as cars, tracks, traffic lights, lanes, and  
|Method|deg_↓_cl_↓_un_↑_no_↑_V_↓_<br>O_↓_<br>L_↓_CR_↓_LA_↓_|
|---|---|
|||
|GDSS-E<br>SG-E<br>Our|1_._05 0_._03 15 34 3_._9 **0****_._66**0_._96 0_._5% 208<br>0_._79 0_._01 39 42 0_._76 1_._27 1_._08 0_._6% 302<br>**0****_._776****_e−_7 51 51 0****_._36** 0_._8 **0****_._080****_._3**%**194**|
```

### Rank 3 (Score: 0.8020)
**Header:** **2 Related Work**
**Content:**
```text
**Discrete and continuous graph diffusion models.** Haefeli et al. (2022) suggest discrete perturbations of the data distribution through a denoising diffusion kernel. Similarly, DiGress (Vignac et al., 2023) uses discrete diffusion methods (Austin et al., 2021) to produce discrete graphs containing categorical node and edge values. Recently, GraphARM (Kong et al., 2023) designed a node-absorbing autoregressive diffusion for efficient and high-quality sampling. Others proposed a discrete diffusion process that utilize graph sparsity to gain efficiency (Chen et al., 2023). While it is argued that discrete modeling of graphs may be beneficial, it is unclear how to sample real-valued attributes in existing frameworks. We insist that many real-world problems are naturally defined by continuous values, which requires the development of a general graph generative model. To this end, several works have proposed score-based methods for graph generation, though they often face limitations in modeling edge attributes. Niu et al. (2020) introduced a permutation-invariant model based on graph neural networks (GNNs) for learning data distributions of adjacency matrices. To extract binary neighborhood information, the real-valued diffusion output is discretized via thresholding. Subsequently, GDSS (Jo et al., 2022) uses separate stochastic differential equations to model node attributes and the adjacency matrix independently. Yet, this separation may limit the information exchange between
```

## Pilar: Objective
**Query:** _Tujuan utama dan sasaran spesifik dari artikel ini adalah untuk memberikan kontribusi baru, mengeksplorasi, dan menyelesaikan permasalahan tersebut._

### Rank 1 (Score: 0.8396)
**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.
```

### Rank 2 (Score: 0.8337)
**Header:** **1 Introduction**
**Content:**
```text
Recent works on score-based models for graph generation have made significant strides but remain limited in scope (Niu et al., 2020; Jo et al., 2022; Fan et al., 2023). While proficient in their designated tasks, these models either completely exclude edge attributes or treat graph components separately, limiting their capacity to capture complex relational structures. We address these limitations with a unified framework that enables the generation of node and edge attributes. An exemplary task with dominant edge attributes is shown in Fig. 1. This task involves translating a driving scene into a graph representation, similar to the approach in VectorNet (Gao et al., 2020). One prominent feature is the existence of edge attributes that introduce relative and interactional information between the road participants. For instance, an edge feature like a “looking at” flag–which indicates if one vehicle is actively observing another–captures situational awareness crucial in dense traffic. This relational cue, such as mutual acknowledgment during lane changes, is necessary for accurately modeling driving behavior but emerges only from the interaction itself, making it impossible to derive from node features alone. This and less expressive versions of such representation are prevalent in motion prediction literature (Huang et al., 2022), with models utilizing it often leading the prediction task leaderboards (Caesar et al., 2020). Consequently, we are interested in generating
```

### Rank 3 (Score: 0.8287)
**Header:** **B.4.2 GDSS-E Baseline Implementation Details**
**Content:**
```text
**GDSS architecture.** For a detailed explanation of their architectural choices, please refer to Section 3.2 of their paper. Briefly, due to the multiple SDE equation formulation, their model employs two separate Graph Neural Networks (GNNs): one for learning the score of the adjacency matrix and another for learning the node features. In contrast, our approach suggests using a single GNN to jointly model both graph components. Aside from this key difference, our architecture is constructed similarly to GDSS, with the addition of an edge-information propagation mechanism within the GNN’s building block, as outlined in Sec. 4.2. Finally, we outline below additional differences and adjustments needed to adapt GDSS to the tasks and context of our paper.  
**Directed graphs.** To adapt GDSS to a directed graph, we need to delete the symmetry inductive bias of the method. First, we deleted the symmetry inductive biases in the attention module of the backbone architecture. In addition, the generated noise and the whole diffusion process are set to be symmetric, meaning they generate symmetric noise patterns. Therefore, we change all aspects of the diffusion and generation process to be a normal Gaussian injection, similar to regular diffusion methods.
```

## Pilar: Futurework
**Query:** _Keterbatasan dalam penelitian ini membuka peluang bagi studi selanjutnya. Penelitian di masa depan disarankan untuk memperluas cakupan dan menyelesaikan isu terkait._

### Rank 1 (Score: 0.8057)
**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: 0.8006)
**Header:** **2 Related Work**
**Content:**
```text
**Discrete and continuous graph diffusion models.** Haefeli et al. (2022) suggest discrete perturbations of the data distribution through a denoising diffusion kernel. Similarly, DiGress (Vignac et al., 2023) uses discrete diffusion methods (Austin et al., 2021) to produce discrete graphs containing categorical node and edge values. Recently, GraphARM (Kong et al., 2023) designed a node-absorbing autoregressive diffusion for efficient and high-quality sampling. Others proposed a discrete diffusion process that utilize graph sparsity to gain efficiency (Chen et al., 2023). While it is argued that discrete modeling of graphs may be beneficial, it is unclear how to sample real-valued attributes in existing frameworks. We insist that many real-world problems are naturally defined by continuous values, which requires the development of a general graph generative model. To this end, several works have proposed score-based methods for graph generation, though they often face limitations in modeling edge attributes. Niu et al. (2020) introduced a permutation-invariant model based on graph neural networks (GNNs) for learning data distributions of adjacency matrices. To extract binary neighborhood information, the real-valued diffusion output is discretized via thresholding. Subsequently, GDSS (Jo et al., 2022) uses separate stochastic differential equations to model node attributes and the adjacency matrix independently. Yet, this separation may limit the information exchange between
```

### Rank 3 (Score: 0.7954)
**Header:** **1 Introduction**
**Content:**
```text
Recent works on score-based models for graph generation have made significant strides but remain limited in scope (Niu et al., 2020; Jo et al., 2022; Fan et al., 2023). While proficient in their designated tasks, these models either completely exclude edge attributes or treat graph components separately, limiting their capacity to capture complex relational structures. We address these limitations with a unified framework that enables the generation of node and edge attributes. An exemplary task with dominant edge attributes is shown in Fig. 1. This task involves translating a driving scene into a graph representation, similar to the approach in VectorNet (Gao et al., 2020). One prominent feature is the existence of edge attributes that introduce relative and interactional information between the road participants. For instance, an edge feature like a “looking at” flag–which indicates if one vehicle is actively observing another–captures situational awareness crucial in dense traffic. This relational cue, such as mutual acknowledgment during lane changes, is necessary for accurately modeling driving behavior but emerges only from the interaction itself, making it impossible to derive from node features alone. This and less expressive versions of such representation are prevalent in motion prediction literature (Huang et al., 2022), with models utilizing it often leading the prediction task leaderboards (Caesar et al., 2020). Consequently, we are interested in generating
```
