327 citations · 845 across the 11 of their papers we have counts for
13 papers
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
Miaobo Hu, Shuhao Hu, Xiaobo Guo +5
A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. W…
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi +4
Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology,…
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train…
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
Vijay Prakash Dwivedi, Charilaos Kanatsoulis, Shenyang Huang +1
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social netwo…
Large Language Models are Good Relational Learners
Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, yet their application to relational deep learning (RDL) remains underexplored. Existi…
Relational Graph Transformer
Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen +5
Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal…
Graph Transformers for Large Graphs
Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu +3
Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, thes…
Union Subgraph Neural Networks
Jiaxing Xu, Aihu Zhang, Qingtian Bian +2
Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Wei…
Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin +4
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.…
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi +3
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph…
Graph Neural Networks with Learnable Structural and Positional Representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent +2
Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender syst…
A Generalization of Transformer Networks to Graphs
Vijay Prakash Dwivedi, Xavier Bresson
We propose a generalization of transformer neural network architecture for arbitrary graphs. The original transformer was designed for Natural Language Processing (NLP), which oper…
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu +3
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive…