Discussing the article: "Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS"

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Check out the new article: Neural Networks in Trading: Node-Adaptive Graph Representation with NAFS.

We invite you to get acquainted with the NAFS (Node-Adaptive Feature Smoothing) method, which is a non-parametric approach to creating node representations that does not require parameter training. NAFS extracts features of each node given its neighbors and then adaptively combines these features to form a final representation.

In recent years, graph representation learning has been widely applied in various application scenarios such as node clustering, link prediction, node classification, and graph classification. The goal of graph representation learning is to encode graph information into node embeddings. Traditional methods for graph representation learning have primarily focused on preserving information about the graph structure. However, these methods face two major limitations:

1. Shallow architecture. While Graph Convolutional Networks (GCNs) employ multiple layers to capture deep structural information, increasing the number of layers often leads to over-smoothing, resulting in indistinguishable node embeddings.
2. Poor scalability. GNN-based graph representation learning methods may fail to scale to large graphs due to high computational costs and significant memory consumption.

The authors of the paper "NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning" set out to address these issues by introducing a novel graph representation method based on simple feature smoothing followed by adaptive combination. The Node-Adaptive Feature Smoothing (NAFS) method generates superior node embeddings by integrating both the graph's structural information and node features. Based on the observation that different nodes exhibit highly varied "smoothing speeds", NAFS adaptively smooths each node's features, using both low- and high-order neighborhood information. Furthermore, feature ensembles are used to combine smoothed features extracted using different smoothing operators. Since NAFS requires no training, it significantly reduces training costs and scales efficiently to large graphs.

Author: Dmitriy Gizlyk