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Workshop
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Semi-Supervised Graph Imbalanced Regression
Gang Liu · Tong Zhao · Eric Inae · Tengfei Luo · Meng Jiang
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Poster
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Tue 8:45
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Regularization properties of adversarially-trained linear regression
Antonio Ribeiro · Dave Zachariah · Francis Bach · Thomas Schön
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Workshop
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Exploring the Properties and Structure of Real Knowledge Graphs across Scientific Disciplines
Nedelina Teneva · Estevam Hruschka
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Workshop
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AnisoGNN: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals
Guangyu Hu · Marat Latypov
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Workshop
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Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
Sagar Srinivas Sakhinana · Venkataramana Runkana
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Workshop
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DspGNN: Bringing Spectral Design to Discrete Time Dynamic Graph Neural Networks for Edge Regression
Leshanshui Yang · Clement Chatelain · Sébastien Adam
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Workshop
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Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs
Nedelina Teneva · Estevam Hruschka
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Workshop
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HoloNets: Spectral Convolutions do extend to Directed Graphs
Christian Koke · Daniel Cremers
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Poster
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Thu 15:00
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Network Regression with Graph Laplacians
Yidong Zhou · Hans-Georg Müller
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Workshop
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Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
Sagar Srinivas Sakhinana · Venkataramana Runkana
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Workshop
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Fri 7:50
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Investigating extrapolation and low-data challenges via contrastive learning of chemical compositions
Federico Ottomano · Giovanni De Felice · Rahul Savani · Vladimir Gusev · Matthew Rosseinsky
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Poster
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Tue 15:15
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Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao · Mangpo Phothilimthana · Sami Abu-El-Haija · Dustin Zelle · Yanqi Zhou · Charith Mendis · Jure Leskovec · Bryan Perozzi
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