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Generating Directed Graphs with Dual Attention and Asymmetric Encoding

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
graph generationdirected graphsflow matchingdiscrete diffusion
Abstract:

Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, or visual understanding. Generating such graphs enables simulation, data augmentation and novel instance discovery; however, this task remains underexplored. We identify two key reasons: first, modeling edge directionality introduces a substantially larger dependency space, making the underlying distribution harder to learn; second, the absence of standardized benchmarks hinders rigorous evaluation. Addressing the former limitation requires more expressive models that are sensitive to directional topologies. Thus, we propose Directo, the first generative model for directed graphs built upon the discrete flow matching framework. Our approach combines: (i) a dual-attention mechanism distinctly capturing incoming and outgoing dependencies, (ii) a robust, discrete generative framework, and (iii) principled positional encodings tailored to asymmetric pairwise relations. To address the second limitation and support evaluation, we introduce a novel and extensive benchmark suite covering synthetic and real-world datasets. Experiments show that our method outperforms existing directed graph generation approaches across diverse settings and competes with specialized models for particular classes, such as directed acyclic graphs. These results highlight the effectiveness and generality of our approach, establishing a solid foundation for future research in directed graph generation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Directo, a discrete flow matching framework for generating directed graphs with dual attention and asymmetric positional encodings. It resides in the 'Dual Attention Mechanisms for Directed Graphs' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Directed Graph Representation Learning', one of three main branches in the field. The small number of sibling papers suggests that dual attention approaches for directed graphs remain an emerging area, though the taxonomy also reveals parallel work in complex embeddings and application-specific architectures.

The taxonomy shows neighboring leaves focused on complex-valued embeddings for directed graphs and application-driven architectures for recommendation or spatial extraction. The paper's position in the representation learning branch distinguishes it from domain-specific models, while its dual attention focus differentiates it from embedding-only methods. The taxonomy's scope notes clarify that this leaf excludes application-specific designs and non-attention embedding techniques, positioning the work as a general-purpose representation method. Nearby branches explore sequence modeling with directional attention, suggesting potential cross-pollination between graph and sequence domains.

Among twenty candidates examined, the dual attention mechanism contribution shows one refutable candidate out of ten examined, indicating some prior work overlap in this specific component. The benchmark suite contribution examined ten candidates with zero refutations, suggesting relative novelty in evaluation infrastructure for directed graph generation. The flow-based generative model contribution was not examined against candidates, leaving its novelty assessment incomplete within this limited search scope. The statistics reflect a focused search rather than exhaustive coverage, with the dual attention component appearing most connected to existing literature among the examined candidates.

Based on the limited search of twenty candidates, the work appears to combine established dual attention ideas with novel application to discrete flow matching for directed graph generation. The sparse taxonomy leaf and low refutation rate for the benchmark contribution suggest meaningful gaps in existing evaluation infrastructure. However, the analysis scope remains constrained to top-K semantic matches and does not capture the full landscape of graph generation or discrete flow methods.

Taxonomy

Core-task Taxonomy Papers
6
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Generating directed graphs with dual attention and asymmetric encoding. The field structure reflects a division into three main branches. Directed Graph Representation Learning focuses on foundational methods that capture asymmetric relationships inherent in directed graphs, often employing dual attention mechanisms to distinguish between incoming and outgoing edges. Application-Specific Directed Graph Architectures tailors these representations to concrete domains such as recommendation systems or spatial network extraction, where directionality carries domain-specific semantics. Sequence Modeling with Directional Dual Attention explores how dual attention can be adapted to sequential data with inherent directional dependencies. Representative works like Directed Graph Transformers[1] and Duplex Dual GAT[3] illustrate how dual attention can be integrated into graph neural architectures to respect edge directionality, while application-driven studies such as Product Recommendation GNN[4] and Road Extraction Asymmetric[5] demonstrate the practical value of asymmetric encoding in specialized settings. A particularly active line of work centers on designing dual attention mechanisms that separately aggregate information along forward and backward edges, enabling richer representations of directed structures. Trade-offs emerge between computational complexity and expressiveness, as well as between general-purpose architectures and domain-tuned designs. Dual Attention Asymmetric[0] sits within the Directed Graph Representation Learning branch, specifically among methods that employ dual attention for directed graphs. Its emphasis on asymmetric encoding aligns closely with Duplex Dual GAT[3], which also leverages dual attention to handle directionality, and with Directionality Graph Transformers[6], which explores similar directional mechanisms. Compared to these neighbors, Dual Attention Asymmetric[0] appears to focus on the interplay between dual attention and asymmetric encoding strategies, potentially offering a more integrated approach to capturing directional dependencies in graph generation tasks.

Claimed Contributions

DIRECTO: first flow-based generative model for directed graphs

The authors introduce DIRECTO, the first generative model for directed graphs built on discrete flow matching. It combines a dual-attention mechanism for capturing incoming and outgoing dependencies with positional encodings designed for asymmetric pairwise relations.

0 retrieved papers
Benchmark suite for directed graph generation evaluation

The authors propose a comprehensive benchmarking framework for rigorous evaluation of directed graph generation, including synthetic datasets with different distributions and real-world datasets for neural architecture search and scene understanding, along with tailored evaluation metrics.

10 retrieved papers
Dual attention mechanism for directional dependencies

The authors design a dual attention block that performs cross-attention between edge features and their reversed counterparts, enabling the model to capture both source-to-target and target-to-source information flow in directed graphs.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DIRECTO: first flow-based generative model for directed graphs

The authors introduce DIRECTO, the first generative model for directed graphs built on discrete flow matching. It combines a dual-attention mechanism for capturing incoming and outgoing dependencies with positional encodings designed for asymmetric pairwise relations.

Contribution

Benchmark suite for directed graph generation evaluation

The authors propose a comprehensive benchmarking framework for rigorous evaluation of directed graph generation, including synthetic datasets with different distributions and real-world datasets for neural architecture search and scene understanding, along with tailored evaluation metrics.

Contribution

Dual attention mechanism for directional dependencies

The authors design a dual attention block that performs cross-attention between edge features and their reversed counterparts, enabling the model to capture both source-to-target and target-to-source information flow in directed graphs.

Table of Contents