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

Kaléu Delphino
2021 arXiv   pre-print
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
more » ... eddings. 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.
arXiv:2102.11691v1 fatcat:jmp35wwf6zgsbcwfimisd4gfti