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ORCID3rd LoG 2024: Virtual Event
- Guy Wolf, Smita Krishnaswamy:
Learning on Graphs Conference, 26-29 November 2024, Virtual. Proceedings of Machine Learning Research 269, PMLR 2024 - Mikhail Mironov, Liudmila Prokhorenkova:
Revisiting Graph Homophily Measures. 1 - Tamara Cucumides, Daniel Daza, Pablo Barceló, Michael Cochez, Floris Geerts, Juan L. Reutter, Miguel Romero:
UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs. 2 - Frederik Wenkel, Semih Cantürk, Stefan Horoi, Michael Perlmutter, Guy Wolf:
Towards a General Recipe for Combinatorial Optimization With Multi-Filter GNNs. 3 - Ramón Dineth Nartallo-Kaluarachchi, Paul Expert, David Beers, Alexander Strang, Morten L. Kringelbach, Renaud Lambiotte, Alain Goriely:
Decomposing Force Fields as Flows on Graphs Reconstructed From Stochastic Trajectories. 4 - Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis:
What Do GNNs Actually Learn? Towards Understanding Their Representations. 5 - Maria Bånkestad, Jennifer R. Andersson, Sebastian Mair, Jens Sjölund:
Ising on the Graph: Task-Specific Graph Subsampling via the Ising Model. 6 - Alexei Pisacane, Victor-Alexandru Darvariu, Mirco Musolesi:
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies. 7 - JJ Wilson, Maya Bechler-Speicher, Petar Velickovic:
Cayley Graph Propagation. 8 - Jacob Hume, Laura Balzano:
A Spectral Framework for Tracking Communities in Evolving Networks. 9 - Luciano Vinas, Arash A. Amini:
Simple GNNs With Low Rank Non-Parametric Aggregators. 10 - Matthias Kohn, Marcel Hoffmann, Ansgar Scherp:
Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs Without Message Passing. 11 - Yucheng Zhang, Beatrice Bevilacqua, Mikhail Galkin, Bruno Ribeiro:
TRIX: A More Expressive Model for Zero-Shot Domain Transfer in Knowledge Graphs. 12 - O. Duranthon, Lenka Zdeborová:
Asymptotic Generalization Error of a Single-Layer Graph Convolutional Network. 13 - Andreas Roth, Franka Bause, Nils Morten Kriege, Thomas Liebig:
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph. 14 - Éanna Curran, Saurabh Ray, Deepak Ajwani:
Effectiveness of SDP Rounding Using Hopfield Networks. 15 - Vincenzo Marco De Luca, Antonio Longa, Pietro Lio, Andrea Passerini:
xAI-Drop: Don't Use What You Cannot Explain. 16 - Diana Gomes, Ann Nowé, Peter Vrancx:
Understanding Feature/Structure Interplay in Graph Neural Networks. 17 - Junze Zhu, Zhongyi Hu, Fan Zhang:
Knowledge Graph Preference Contrastive Learning for Recommendation. 18 - Jiahui Liu, Zhenkun Cai, Zhiyong Chen, Minjie Wang:
DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs. 19 - Sarthak Malik, Aditi Rai, Ram Ganesh V, Himank Sehgal, Akshay Sethi, Aakarsh Malhotra:
GraTeD-MLP: Efficient Node Classification via Graph Transformer Distillation to MLP. 20 - Guillaume Dalle, Patrick Thiran:
Optimal Performance of Graph Convolutional Networks on the Contextual Stochastic Block Model. 21 - Or Feldman, Chaim Baskin:
Leveraging Temporal Graph Networks Using Module Decoupling. 22 - Kaan Sancak, Muhammed Fatih Balin, Ümit V. Çatalyürek:
Do We Really Need Complicated Graph Learning Models? - A Simple but Effective Baseline. 23 - Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu:
A Pure Transformer Pretraining Framework on Text-Attributed Graphs. 24 - Eric Qu, Lige Zhang, Habib Debaya, Yue Wu, Dongmian Zou:
Hyperbolic Kernel Convolution: A Generic Framework. 25 - Csaba Both, Nima Dehmamy, Jianzhi Long, Rose Yu:
Faster Optimization on Sparse Graphs via Neural Reparametrization. 26 - Chunshu Wu, Ruibing Song, Chuan Liu, Yuqing Wang, Yousu Chen, Ang Li, Dongfang Liu, Ying Nian Wu, Michael Huang, Tong Geng:
NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting. 27 - Shenyang Huang, Farimah Poursafaei, Reihaneh Rabbany, Guillaume Rabusseau, Emanuele Rossi:
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs. 28 - Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang:
Flexible Diffusion Scopes With Parameterized Laplacian for Heterophilic Graph Learning. 29 - Naganand Yadati:
Oversquashing in Hypergraph Neural Networks. 30 - Maysam Behmanesh, Maks Ovsjanikov:
Smoothed Graph Contrastive Learning via Seamless Proximity Integration. 31 - Arnab Kumar Mondal, Jay Nandy, Manohar Kaul, Mahesh Chandran:
Stochastic Experience-Replay for Graph Continual Learning. 32 - Minho Lee, Yun Young Choi, Sun Woo Park, Seunghwan Lee, Joohwan Ko, Jaeyoung Hong:
Enhancing Topological Dependencies in Spatio-Temporal Graphs With Cycle Message Passing Blocks. 33 - Zhongtian Ma, Qiaosheng Zhang, Zhen Wang:
Matrix Completion With Hypergraphs: Sharp Thresholds and Efficient Algorithms. 34 - Qian Ma, Haitao Mao, Jingzhe Liu, Zhehua Zhang, Chunlin Feng, Yu Song, Yihan Shao, Yao Ma:
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning? 35 - Hang Li, Wei Jin, Geri Skenderi, Harry Shomer, Wenzhuo Tang, Wenqi Fan, Jiliang Tang:
Sub-Graph Based Diffusion Model for Link Prediction. 36 - Mustafa Munir, Alex Zhang, Radu Marculescu:
Multi-Scale High-Resolution Logarithmic Grapher Module for Efficient Vision GNNs. 37 - Shrimon Mukherjee, Madhusudan Ghosh, Partha Basuchowdhuri:
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction. 38 - Yuhong Luo, Pan Li:
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling. 39 - Jiashu He, Charilaos I. Kanatsoulis, Alejandro Ribeiro:
T-Gae: Transferable Graph Autoencoder for Network Alignment. 40 - Eric Inae, Gang Liu, Meng Jiang:
Motif-Aware Attribute Masking for Molecular Graph Pre-Training. 41 - Rubén Ballester, Bastian Rieck:
On the Expressivity of Persistent Homology in Graph Learning. 42 - Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu:
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models. 43 - Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang:
Towards Neural Scaling Laws on Graphs. 44 - Davide Buffelli, Farzin Soleymani, Bastian Rieck:
CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs. 45 - Malte Luttermann, Ralf Möller, Marcel Gehrke:
Lifted Model Construction Without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs. 46 - Julia Gastinger, Timo Sztyler, Nils Steinert, Sabine Gründer-Fahrer, Michael Martin, Anett Schuelke, Heiner Stuckenschmidt:
Dynamic Representations of Global Crises: A Temporal Knowledge Graph for Conflicts, Trade and Value Networks. 47
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