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Found 14 papers, 5 papers with code
LLM-Augmented Graph Neural Recommenders: Integrating User Reviews
Recommender systems increasingly aim to combine signals from both user reviews and purchase (or other interaction) behaviors.
GEFM: Graph-Enhanced EEG Foundation Model
To address this limitation, we propose Graph-Enhanced EEG Foundation Model (GEFM), a novel foundation model for EEG that integrates both temporal and inter-channel information.
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
In diagnosing neurological disorders from electroencephalography (EEG) data, foundation models such as Transformers have been employed to capture temporal dynamics.
Multimodal Point-of-Interest Recommendation
A model trained on this semi-multimodal dataset has outperformed another model trained on the same dataset without picture descriptions.
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs).
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks
Specifically, we evaluated the model performance of representative homogeneous GNN models which consider single-type nodes and edges and heterogeneous GNN models which support different types of nodes and edges.
How Expressive are Transformers in Spectral Domain for Graphs?
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
SpecTRA: Spectral Transformer for Graph Representation Learning
Transformers have recently been applied in the more generic domain of graphs.
Global Data Science Project for COVID-19
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
The Impact of COVID-19 on Flight Networks
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.
Ranked #5 on
Dynamic Link Prediction
on DBLP Temporal
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.
Databases
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150, 000 people since 2006, upwards of 700, 000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people.
