MultiWalk: A Framework to Generate Node Embeddings Based on an Ensemble of Walk Methods

Kaléu Delphino

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arXiv · 2102.11691

MultiWalk: A Framework to Generate Node Embeddings Based on an Ensemble of Walk Methods

Kaléu Delphino

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Abstract

Graph embeddings are low dimensional representations of nodes, edges or whole graphs. Such representations allow for data in a network format to be used along with machine learning models for a variety of tasks (e.g., node classification), where using a similarity matrix would be impractical. In recent years, many methods for graph embedding generation have been created based on the idea of random walks. We propose MultiWalk, a framework that uses an ensemble of these methods to generate the embeddings. Our experiments show that the proposed framework, using an ensemble composed of two state-of-the-art methods, can generate embeddings that perform better in classification tasks than each method in isolation.

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Kaléu Delphino. 2021-02-23. MultiWalk: A Framework to Generate Node Embeddings Based on an Ensemble of Walk Methods. https://arxiv.org/abs/2102.11691

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