Neighbor2vec: an efficient and effective method for Graph Embedding

TL;DR

Neighbor2vec introduces a neighbor-based sampling strategy for graph embedding, significantly improving accuracy and scalability in node classification and link prediction tasks across various networks.

Contribution

It presents a simple, effective framework that enhances graph embedding quality and scalability, surpassing existing state-of-the-art unsupervised methods.

Findings

Achieves up to 6.8% higher accuracy in node classification

Outperforms all baselines and classical GNNs in experiments

Demonstrates improved scalability and effectiveness across multiple datasets

Abstract

Graph embedding techniques have led to significant progress in recent years. However, present techniques are not effective enough to capture the patterns of networks. This paper propose neighbor2vec, a neighbor-based sampling strategy used algorithm to learn the neighborhood representations of node, a framework to gather the structure information by feature propagation between the node and its neighbors. We claim that neighbor2vec is a simple and effective approach to enhancing the scalability as well as equality of graph embedding, and it breaks the limits of the existing state-of-the-art unsupervised techniques. We conduct experiments on several node classification and link prediction tasks for networks such as ogbn-arxiv, ogbn-products, ogbn-proteins, ogbl-ppa,ogbl-collab and ogbl-citation2. The result shows that Neighbor2vec's representations provide an average accuracy scores up to 6.8 percent higher than competing methods in node classification tasks and 3.0 percent higher in link prediction tasks. The neighbor2vec's representations are able to outperform all baseline methods and two classical GNN models in all six experiments.