Representation of the structure of graphs by sequences of instructions

2025-12-11 · Ezequiel Lopez-Rubio arxiv

The representation of graphs is commonly based on the adjacency matrix concept. This formulation is the foundation of most algebraic and computational approaches to graph processing. The advent of deep learning language models offers a wide range of powerful computational models that are specialized in the processing of text. However, current procedures to represent graphs are not amenable to processing by these models. In this work, a new method to represent graphs is proposed. It represents the adjacency matrix of a graph by a string of simple instructions. The instructions build the adjacency matrix step by step. The transformation is reversible, i.e., given a graph the string can be produced and vice versa. The proposed representation is compact, and it maintains the local structural patterns of the graph. Therefore, it is envisaged that it could be useful to boost the processing of graphs by deep learning models. A tentative computational experiment is reported, demonstrating improved classification performance and faster computation times with the proposed representation.

📄 PDF Abstract BibTeX arXiv:2512.10429

Code (0)

등록된 구현이 없습니다.

Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD

2026-04-11 · Shengjie Gong, Wenjie Peng, Hongyuan Chen, Gangyu Zhang 외 arxiv

Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing methods typically decode text directly into executable code (e.g., bpy) w…

Code Generation

Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction

2018-02-14 · Anh Viet Phan, Minh Le Nguyen, Lam Thu Bui

Existing defects in software components is unavoidable and leads to not only a waste of time and money but also many serious consequences. To build predictive models, previous studies focus on manually extracting feature…

Zero-Shot Instruction Following in RL via Structured LTL Representations

2025-12-02 · Mattia Giuri, Mathias Jackermeier, Alessandro Abate arxiv

Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents. Recent work has shown that interpreting LTL instructions as finite automata, which ca…

Reinforcement Learning Instruction Following Graph Neural Network

Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models

2025-10-18 · Chenrui Tie, Shengxiang Sun, Yudi Lin, Yanbo Wang 외 arxiv

Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Connections represent the foundational physi…

Classifying Malware Using Function Representations in a Static Call Graph

2020-12-01 · Thomas Dalton, Mauritius Schmidtler, Alireza Hadj Khodabakhshi

We propose a deep learning approach for identifying malware families using the function call graphs of x86 assembly instructions. Though prior work on static call graph analysis exists, very little involves the applicati…

Feature Engineering Malware Classification Malware Family Detection