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Found 13 papers, 3 papers with code
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves over time.
BeMap: Balanced Message Passing for Fair Graph Neural Network
Fairness in graph neural networks has been actively studied recently.
Do We Really Need Complicated Model Architectures For Temporal Networks?
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection
As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important.
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy.
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
To solve the performance degradation, we propose to apply $\text{{Global Server Corrections}}$ on the server to refine the locally learned models.
On Provable Benefits of Depth in Training Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing.
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
In this paper, we describe and analyze a general doubly variance reduction schema that can accelerate any sampling method under the memory budget.
On the Importance of Sampling in Training GCNs: Convergence Analysis and Variance Reduction
In this paper, we describe and analyze a general \textbf{\textit{doubly variance reduction}} schema that can accelerate any sampling method under the memory budget.
GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
Sampling-based methods promise scalability improvements when paired with stochastic gradient descent in training Graph Convolutional Networks (GCNs).
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks
In this paper, we theoretically analyze the variance of sampling methods and show that, due to the composite structure of empirical risk, the variance of any sampling method can be decomposed into \textit{embedding approximation variance} in the forward stage and \textit{stochastic gradient variance} in the backward stage that necessities mitigating both types of variance to obtain faster convergence rate.
Scene Graph Generation via Conditional Random Fields
Scene graph, a graph representation of images that captures object instances and their relationships, offers a comprehensive understanding of an image.
Improved Face Detection and Alignment using Cascade Deep Convolutional Network
Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression.
