Graph Positional Encoding

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17 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion
Oct 21, 2026Geri SkenderiRepresentation Learning Message Passing Neural Networks
HASH
Scaling Graph Reasoning in Language Models with Learnable Graph Positional Encodings
Oct 20, 2026Javier Porras-Valenzuela, Arush Arora, Alejandro RibeiroLLM Reasoning with Graphs Graph Positional Encoding
University of Pennsylvania
Path Laplacian Encodings for Directed Graphs
Oct 3, 2026Lydia Mezrag, Semih Cantürk, Michael Perlmutter +2Graph Positional Encoding Positional Encoding
Université de Montréal · Mila – Quebec AI Institute · Boise State University +1
Higher-Order Positional Encodings for Graph Representation Learning
Oct 1, 2026Caleb Stam, Aagrim Hoysal, Sanjukta KrishnagopalGraph Representation Learning Positional Encoding
University of California, Santa Barbara
Retargeting Motions to Diverse Skeletons via Learnable Flattening
Sep 29, 2026Kia-Jüng Yang, Fabian H. Sinz, Paweł A. PierzchlewiczGraph Positional Encoding Zero-Shot Learning
Institute of Computer Science, University of Göttingen · Campus Institute Data Science, University Göttingen · Pantomim P.S.A
Analytic-Walk Rotary Positional Encodings for Graphs
Sep 26, 2026Jiaqing Xie, Yuxin Wang, Xipeng QiuGraph Positional Encoding Rotary Positional Embeddings
Shanghai Innovation Institute · Fudan University
Converse and Collision-Based Achievability for Node Localization with Hybrid Distance-Spectral Graph Positional Encodings
Aug 31, 2026Zimo Yan, Yifan Li, Hao Li +4Representation Identifiability Positional Encoding
National University of Defense Technology, Changsha, China · Wuhan University, Wuhan, China
Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
Jul 31, 2026Sen Zhao, Cheng Liu, Shuyin Xia +4Graph Positional Encoding Graph Representation Learning
Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China · Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China · Chongqing Ant ConsumerFinance Co,. Ltd, Ant Group, Chongqing, 400065, China
Eigenbasis-Independent Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
Jul 8, 2026Jiaqing Xie, Yuxin WangPositional Encoding Spectral Methods
Fudan University
Communicability-Inspired Positional Encoding (CIPE)
Jun 24, 2026Yipeng Zhang, Zhongtian Sun, Pietro Liò +1Graph Positional Encoding Graph Representation Learning
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore · School of Computing, University of Kent, Canterbury, Kent CT2 7NZ, UK · Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
Understanding Truncated Positional Encodings for Graph Neural Networks
Jun 11, 2026James Flora, Mitchell Black, Weng-Keen Wong +1GNN Expressivity Positional Encoding
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA · Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, California, USA.
When Do Graph Foundation Models Transfer? A Data-Centric Theory
May 28, 2026Jiajun Zhu, Ying Chen, Peihao Wang +4Graph Positional Encoding Graphon Theory
University of Texas at Austin · Arizona State University · Georgia Institute of Technology
Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting
May 13, 2026Chanyoung Kim, Donghyun Kim, Dong-Hyun Sim +2Graph Positional Encoding Graph Attention Networks
Emory University · Yonsei University · WHATs Lab
Rethinking Positional Encoding for Neural Vehicle Routing
May 12, 2026Chuanbo Hua, Federico Berto, Andre Hottung +8Vehicle Routing Positional Encoding
KAIST · Radical Numerics · Bielefeld University +3
Size-Transferability of Graph Transformers with Convolutional Positional Encodings
Feb 16, 2026Javier Porras-Valenzuela, Zhiyang Wang, Teresa Shang +2Graph Positional Encoding Graph Transformer
Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA · Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
Rotary Position Encodings for Graphs
Sep 26, 2025Isaac Reid, Arijit Sehanobish, Cederik Höfs +7Graph Positional Encoding Rotary Positional Embeddings
University of Cambridge · Google DeepMind · Independent Researcher +3
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework
Mar 2, 2024Junxian Li, Bin Shi, Erfei Cui +2Graph Positional Encoding Knowledge Distillation
Xi’an Jiaotong University · Arizona State University, Tempe, USA · Tongji University, Shanghai, China

Oct 21, 2026J/K move · Enter open · S save

Structure-Aware Message Passing on Petri Nets through Directed Walks and Incidence Diffusion

Geri Skenderi

HASH

Representation Learning Message Passing Neural Networks Process Mining Graph Positional Encoding

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A common application of the process mining discipline is to connect observed event data with Petri nets, which are bipartite, directed graphical process models with rich formal underpinnings. Recent work learns vector representations of Petri nets to retrieve process models and relate them to event logs, but leaves implicit two key aspects hidden within the net's structure: directed cycles (process loops) and invariants recorded by the net's incidence matrix. We expose both of these to message-passing graph neural networks, through a positional encoding built from return probabilities of directed place-transition walks (PNRWPE), and through same-type messages gated by diffusion kernels of the incidence matrix (DA-PNGNN). Our constructions raise macro balanced accuracy in predicting the generation factors of the 96 Process Discovery Contest 2023 models from 0.751 to 0.796 with PNRWPE, which nearly doubles accuracy on process loops, and to 0.830 with DA-PNGNN, which, without retuning, also improves net-to-tree retrieval top-1 accuracy on the self-supervised ProcRosetta benchmark by 4.1 to 5.9 points.