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Found 26 papers, 17 papers with code
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data.
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One-shot Federated Learning Methods: A Practical Guide
One-shot Federated Learning (OFL) is a distributed machine learning paradigm that constrains client-server communication to a single round, addressing privacy and communication overhead issues associated with multiple rounds of data exchange in traditional Federated Learning (FL).
Automatic Pruning via Structured Lasso with Class-wise Information
For instance, using the VGG16 model on the CIFAR-10 dataset, we achieve a parameter reduction of 85%, a decrease in FLOPs by 61%, and maintain an accuracy of 94. 10% (0. 14% higher than the original model); we reduce the parameters by 55% with the accuracy at 76. 12% using the ResNet architecture on ImageNet (only drops 0. 03%).
A Unified Invariant Learning Framework for Graph Classification
It provides a unified perspective on invariant graph learning, emphasizing both structural and semantic invariance principles to identify more robust stable features.
Partitioning Message Passing for Graph Fraud Detection
Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks.
ED-ViT: Splitting Vision Transformer for Distributed Inference on Edge Devices
We conduct extensive experiments on five datasets with three model structures, demonstrating that our approach significantly reduces inference latency on edge devices and achieves a model size reduction of up to 28. 9 times and 34. 1 times, respectively, while maintaining test accuracy comparable to the original Vision Transformer.
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs).
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby broadening the scope and potential of GRL.
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
The techniques can be broadly categorized into meta-learning, pre-training, and hybrid approaches, with a finer-grained classification in each category to aid readers in their method selection process.
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design.
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs.
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Based on this framework, we propose a prompt-based transferability test to find the most relevant pretext task in order to reduce the semantic gap.
EX-Graph: A Pioneering Dataset Bridging Ethereum and X
While numerous public blockchain datasets are available, their utility is constrained by an exclusive focus on blockchain data.
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
Unbiased Learning to Rank (ULTR) aims to train unbiased ranking models from biased click logs, by explicitly modeling a generation process for user behavior and fitting click data based on examination hypothesis.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning.
Link Prediction on Latent Heterogeneous Graphs
Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed.
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner.
On Generalized Degree Fairness in Graph Neural Networks
In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes.
Learning to Count Isomorphisms with Graph Neural Networks
However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting.
Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve.
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors.
Node-wise Localization of Graph Neural Networks
Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes.
Count-GNN: Graph Neural Networks for Subgraph Isomorphism Counting
At the graph level, we modulate the graph representation conditioned on the query subgraph, so that the model can be adapted to each unique query for better matching with the input graph.
Meta-Inductive Node Classification across Graphs
That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.
Topological Recurrent Neural Network for Diffusion Prediction
As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.
