PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models - snap-stanford/plurel

Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer messages - is increas...

In recent years, the expressive power of various neural architectures -- including graph neural networks (GNNs), transformers, and recurrent neural networks -- has been characterised using tools from ...

The authors introduce CausalBench, a benchmark suite that enhances network inference evaluation with real-world, large-scale single-cell perturbation data.

Temporal Graph Learning (TGL) has become a robust framework for discovering patterns in dynamic networks and predicting future interactions. While existing research has largely concentrated on learnin...

Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. While a broad range of literature has explored the graph-reaso...

Future link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark datasets have been...

Nowadays, temporal heterogeneous graphs attract much research and industrial attention for building the next-generation Relational Deep Learning models and applications, due to their informative...

Thank you for your interest in reviewing research submissions for the Temporal Graph Learning Workshop@ KDD 2025. Please fill out the information in this form, so we can know more about your backgroun...

Key dates

Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networ...

Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal graphs, either through a stream of edge events or a...

_Graph Neural Tangent Kernel_ (GNTK) fuses graph neural networks and graph kernels, simplifies the process of graph representation learning, interprets the training dynamics of graph neural...

YouTube video by Temporal Graph Learning

YouTube video by Temporal Graph Learning

YouTube video by Temporal Graph Learning

YouTube video by Temporal Graph Learning

Much of the world’s most valued data is stored in relational databases and data warehouses, where the data is organized into tables connected by primary-foreign key relations. However, building mac...