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Found 8 papers, 5 papers with code
H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, known as the Graph Foundation Model (GFM).
ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
Relational deep learning (RDL) settles among the most exciting advances in machine learning for relational databases, leveraging the representational power of message passing graph neural networks (GNNs) to derive useful knowledge and run predicting tasks on tables connected through primary-to-foreign key links.
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance
Then, through prompt engineering and the proposed Balanced RAG method, it effectively detects coordinated disinformation campaigns on X (Twitter).
Personalized Graph-Based Retrieval for Large Language Models
As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.
Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform.
PerfRL: A Small Language Model Framework for Efficient Code Optimization
Code optimization is a challenging task requiring a substantial level of expertise from developers.
