engineer graph edges

Existing graph generation methods neglect edge attribute modeling, limiting their applicability in domains such as transportation that require rich edge features. To address this, we propose the first score-based diffusion framework jointly modeling nodes, edges, and adjacency structure. Our approach introduces a novel node-edge joint attention mechanism that enables bidirectional dependency modeling among all three components throughout the diffusion process, supporting high-fidelity edge attribute generation. Key technical innovations include score distillation, edge-aware diffusion sampling, and joint noise modeling over node and edge variables. Extensive experiments on multiple real-world and synthetic benchmarks—including a newly constructed edge-valued evaluation dataset—demonstrate significant improvements over state-of-the-art methods: 21.3% reduction in edge attribute reconstruction error and 18.7% gain in structural validity. The method has been successfully deployed for traffic scenario graph generation.

This study addresses the underexplored challenge of effectively visualizing bivariate distributions on graph edges under the spatial constraints of adjacency matrix layouts. The work proposes a novel approach that encodes edge-wise bivariate distributions using two statistical summaries: central tendency and dispersion. Through a preregistered crowdsourced experiment, the authors systematically evaluate four compact encoding designs—bivariate color mapping, embedded bar charts, and two superimposed mark types combining area or angle with color. Results demonstrate that the area-based superimposed marks and embedded bar charts yield the best overall performance, while angle-based encodings show moderate but inconsistent accuracy, and bivariate color mappings perform significantly worse. This research provides empirical evidence and practical guidance for designing visualizations of bivariate edge data in graph structures.

Existing graph neural network (GNN) pretraining suffers from objective misalignment with downstream tasks, while mainstream graph prompt-tuning methods operate solely on nodes, neglecting edge structural information and thereby limiting representation quality. To address this, we propose **EdgePrompt**, the first edge-level prompt-tuning framework, which injects learnable edge prompt vectors into the message-passing process to explicitly model edge dependencies. EdgePrompt is model- and pretraining-agnostic, ensuring broad applicability across diverse GNN architectures and pretraining paradigms. We further provide theoretical convergence analysis for both node- and graph-classification tasks. Extensive experiments across 10 benchmark graph datasets and 4 pretraining strategies demonstrate that EdgePrompt consistently outperforms six state-of-the-art baselines. The implementation is publicly available.

This work addresses the disconnect between modular application design and execution in edge and cloud computing, particularly the challenges of uniformly modeling computational units, data sharing, and event dependencies. To bridge this gap, the paper proposes a domain-specific visual graph editor that enables users to define data and control flows through three core abstractions: kernel functions, shared memory nodes, and event triggers. The tool automatically generates deployable, machine-readable representations from these visual models. By integrating explicit execution semantics, modular design, and one-click deployment within a unified interface—combining visual modeling, domain-specific language (DSL) abstractions, event-driven architecture, and distributed shared memory—it significantly enhances the comprehensibility of execution order and dependencies. Evaluations in scenarios such as federated learning demonstrate its superior semantic expressiveness and direct deployability compared to general-purpose diagramming tools and conventional workflow editors.

Traditional regional data modeling often assumes constant spatial dependence strength between adjacent regions, leading to distorted covariance structures; while treating each edge weight as an independent parameter alleviates this issue, it introduces high-dimensional estimation challenges. This paper proposes a low-dimensional basis-function expansion for parameterizing the edge-weight matrix—marking the first application of dimensionality reduction to graph edge-weight estimation—enabling flexible characterization of heterogeneous spatial dependence via a small set of basis coefficients. Integrating graph neural covariance modeling with spatial statistical inference, the method achieves significant improvements in both covariance estimation accuracy and computational efficiency in simulations and empirical studies. It enables robust, scalable modeling of large-scale regional data.

This work addresses the high computational complexity of cut-set computation in multi-path ensemble attribute evaluation by proposing an efficient algorithm and developing a vectorized computing framework based on matrix operations, which reformulates path attribute calculations as parallelizable array operations. For the first time, this approach provides a practical implementation of the formal model for path set attributes, integrating an optimized cut-set algorithm with array-oriented programming languages to substantially improve computational efficiency. Empirical evaluations across network simulations of varying complexity demonstrate that the method yields predictable and acceptable execution times, thereby establishing a practical foundation for large-scale multi-path analysis.

This work addresses the challenge of efficiently compressing edge weights in weighted graph adjacency matrices by proposing a line-graph-based graph signal modeling approach. Specifically, edge weights are treated as graph signals defined on the line graph and are compressed through transform coding using graph filter banks, followed by quantization and entropy coding. The method innovatively introduces an edge smoothness metric that can be computed without explicitly constructing the line graph, enabling effective prediction of compression performance. Experimental results demonstrate that the proposed framework consistently outperforms existing matrix preprocessing techniques on both synthetic and real-world datasets, thereby validating its efficacy and practicality for lossy graph weight compression.

Existing graph analysis systems struggle to effectively integrate topological structure with node attributes, limiting the discovery of patterns driven by their interaction. This work proposes ZipLine, a novel system that, for the first time, unifies predicate logic to express topology, node attributes, and neighborhood relationships within a single formalism. ZipLine introduces an interaction-driven predicate learning algorithm that enables cross-space collaborative reasoning and iterative analysis. By integrating coordinated views, subgraph selection, and attribute brushing techniques, the system facilitates expressive and efficient exploration of complex patterns in multivariate graphs. Empirical evaluation across three real-world domains—energy infrastructure, cybersecurity, and drug discovery—demonstrates ZipLine’s effectiveness in significantly enhancing the expressiveness and discoverability of intricate graph patterns.