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Found 14 papers, 10 papers with code
How to Make LLMs Strong Node Classifiers?
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks.
Retrieval Augmentation via User Interest Clustering
In this paper, we address these challenges by introducing an intermediate "interest" layer between users and items.
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.
Mixture of Weak & Strong Experts on Graphs
We propose to decouple the two modalities by Mixture of weak and strong experts (Mowst), where the weak expert is a light-weight Multi-layer Perceptron (MLP), and the strong expert is an off-the-shelf GNN.
On the Equivalence of Graph Convolution and Mixup
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
In this paper, we propose to accelerate GNN inference by pruning the dimensions in each layer with negligible accuracy loss.
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Accurate, Efficient and Scalable Training of Graph Neural Networks
For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning.
GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms
We first analyze the computation and communication characteristics of various GCN training algorithms, and select a subgraph-based algorithm that is well suited for hardware execution.
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
On the other hand, weight pruning techniques address the redundancy in model parameters by converting dense convolutional kernels into sparse ones.
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Accurate, Efficient and Scalable Graph Embedding
However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency.
