LoG 2025

47 papers

A Pure Transformer Pretraining Framework on Text-Attributed Graphs Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu

A Spectral Framework for Tracking Communities in Evolving Networks Jacob Hume, Laura Balzano

Asymptotic Generalization Error of a Single-Layer Graph Convolutional Network O Duranthon, Lenka Zdeborova

Cayley Graph Propagation Jj Wilson, Maya Bechler-Speicher, Petar Veličković

CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs Davide Buffelli, Farzin Soleymani, Bastian Rieck

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction Shrimon Mukherjee, Madhusudan Ghosh, Partha Basuchowdhuri

Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu

Decomposing Force Fields as Flows on Graphs Reconstructed from Stochastic Trajectories Ramón Dineth Nartallo-Kaluarachchi, Paul Expert, David Beers, Alexander Strang, Morten L Kringelbach, Renaud Lambiotte, Alain Goriely

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Jiahui Liu, Zhenkun Cai, Zhiyong Chen, Minjie Wang

Do Neural Scaling Laws Exist on Graph Self-Supervised Learning? Qian Ma, Haitao Mao, Jingzhe Liu, Zhehua Zhang, Chunlin Feng, Yu Song, Yihan Shao, Yao Ma

Do We Really Need Complicated Graph Learning Models? – A Simple but Effective Baseline Kaan Sancak, Muhammed Fatih Balin, Umit Catalyurek

Dynamic Representations of Global Crises: A Temporal Knowledge Graph for Conflicts, Trade and Value Networks Julia Gastinger, Timo Sztyler, Nils Steinert, Sabine Gründer-Fahrer, Michael Martin, Anett Schuelke, Heiner Stuckenschmidt

Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs Without Message Passing Matthias Kohn, Marcel Hoffmann, Ansgar Scherp

Effectiveness of SDP Rounding Using Hopfield Networks Éanna Curran, Saurabh Ray, Deepak Ajwani

Enhancing Topological Dependencies in Spatio-Temporal Graphs with Cycle Message Passing Blocks Minho Lee, Yun Young Choi, Sun Woo Park, Seunghwan Lee, Joohwan Ko, Jaeyoung Hong

Faster Optimization on Sparse Graphs via Neural Reparametrization Csaba Both, Nima Dehmamy, Jianzhi Long, Rose Yu

Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang

GraTeD-MLP: Efficient Node Classification via Graph Transformer Distillation to MLP Sarthak Malik, Aditi Rai, Ram Ganesh V, Himank Sehgal, Akshay Sethi, Aakarsh Malhotra

Hyperbolic Kernel Convolution: A Generic Framework Eric Qu, Lige Zhang, Habib Debaya, Yue Wu, Dongmian Zou

Ising on the Graph: Task-Specific Graph Subsampling via the Ising Model Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair, Jens Sjölund

Knowledge Graph Preference Contrastive Learning for Recommendation Junze Zhu, Zhongyi Hu, Fan Zhang

Leveraging Temporal Graph Networks Using Module Decoupling Or Feldman, Chaim Baskin

Lifted Model Construction Without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs Malte Luttermann, Ralf Möller, Marcel Gehrke

Matrix Completion with Hypergraphs: Sharp Thresholds and Efficient Algorithms Zhongtian Ma, Qiaosheng Zhang, Zhen Wang

Motif-Aware Attribute Masking for Molecular Graph Pre-Training Eric Inae, Gang Liu, Meng Jiang

Multi-Scale High-Resolution Logarithmic Grapher Module for Efficient Vision GNNs Mustafa Munir, Alex Zhang, Radu Marculescu

NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting Chunshu Wu, Ruibing Song, Chuan Liu, Yuqing Wang, Yousu Chen, Ang Li, Dongfang Liu, Ying Nian Wu, Michael Huang, Tong Geng

On the Expressivity of Persistent Homology in Graph Learning Rubén Ballester, Bastian Rieck

Optimal Performance of Graph Convolutional Networks on the Contextual Stochastic Block Model Guillaume Dalle, Patrick Thiran

Oversquashing in Hypergraph Neural Networks Naganand Yadati

Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph Andreas Roth, Franka Bause, Nils Morten Kriege, Thomas Liebig

Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies Alexei Pisacane, Victor-Alexandru Darvariu, Mirco Musolesi

Revisiting Graph Homophily Measures Mikhail Mironov, Liudmila Prokhorenkova

Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling Yuhong Luo, Pan Li

Simple GNNs with Low Rank Non-Parametric Aggregators Luciano Vinas, Arash A. Amini

Smoothed Graph Contrastive Learning via Seamless Proximity Integration Maysam Behmanesh, Maks Ovsjanikov

Stochastic Experience-Replay for Graph Continual Learning Arnab Kumar Mondal, Jay Nandy, Manohar Kaul, Mahesh Chandran

Sub-Graph Based Diffusion Model for Link Prediction Hang Li, Wei Jin, Geri Skenderi, Harry Shomer, Wenzhuo Tang, Wenqi Fan, Jiliang Tang

T-Gae: Transferable Graph Autoencoder for Network Alignment Jiashu He, Charilaos Kanatsoulis, Alejandro Ribeiro

Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs Frederik Wenkel, Semih Cantürk, Stefan Horoi, Michael Perlmutter, Guy Wolf

Towards Neural Scaling Laws on Graphs Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang

TRIX: A More Expressive Model for Zero-Shot Domain Transfer in Knowledge Graphs Yucheng Zhang, Beatrice Bevilacqua, Mikhail Galkin, Bruno Ribeiro

Understanding Feature/Structure Interplay in Graph Neural Networks Diana Gomes, Ann Nowe, Peter Vrancx

UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs Tamara Cucumides, Daniel Daza, Pablo Barcelo, Michael Cochez, Floris Geerts, Juan L Reutter, Miguel Romero Orth

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, Guillaume Rabusseau, Emanuele Rossi

What Do GNNs Actually Learn? Towards Understanding Their Representations Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis

xAI-Drop: Don’t Use What You Cannot Explain Vincenzo Marco De Luca, Antonio Longa, Pietro Lio, Andrea Passerini