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Found 13 papers, 9 papers with code
How Can Large Language Models Understand Spatial-Temporal Data?
We tackle the data mismatch by proposing: 1) STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graph data into concise tokens capturing both spatial and temporal relationships; 2) STG-Adapter: This minimalistic adapter, consisting of linear encoding and decoding layers, bridges the gap between tokenized data and LLM comprehension.
Spatio-Temporal Graph Mixformer for Traffic Forecasting
Additionally, we train an estimator model that express the contribution of a node over the desired prediction.
Ranked #1 on
Traffic Prediction
on METR-LA
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Embedding models have shown great power in knowledge graph completion (KGC) task.
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e. g., filtering in Graph Fourier Transforms.
Ranked #4 on
Graph Regression
on ZINC
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
First, a spatial-temporal graph neural network is proposed, which can capture the node-specific spatial-temporal traffic patterns of different road networks.
Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks
Task-relevant structures can be $localized$ or $sparse$ which are only involved in subgraphs or characterized by the interactions of subgraphs (a hierarchical perspective).
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix, which motivates us to directly study the characteristics of the spectrum and their effects on GNN performance.
Ranked #3 on
Graph Classification
on ENZYMES
Do Transformers Really Perform Badly for Graph Representation?
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track
In this technical report, we present our solution of KDD Cup 2021 OGB Large-Scale Challenge - PCQM4M-LSC Track.
Do Transformers Really Perform Bad for Graph Representation?
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
Ranked #1 on
Graph Regression
on PCQM4M-LSC
Breaking the Expressive Bottlenecks of Graph Neural Networks
Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives.
