Graph representation learning (GRL) aims to map a graph into a low-dimensional vector space while preserving graph topology and node properties. This study proposes a novel GRL model, Multi-Level GRL (simply, ML-GRL), that recursively partitions input graphs by selecting the most appropriate community detection algorithm at each graph or partitioned subgraph. To preserve the relationship between subgraphs, ML-GRL incorporates global graphs that effectively maintain the overall topology. ML-GRL employs a prediction model, which is pre-trained using graph-based features and covers a wide range of graph distributions, to estimate GRL accuracy of each community detection algorithm without partitioning graphs or subgraphs and evaluating them. ML-GRL improves learning accuracy by selecting the most effective community detection algorithm while enhancing learning efficiency from parallel processing of partitioned subgraphs. Through extensive experiments with two different tasks, we demonstrate ML-GRL's superiority over the six representative GRL models in terms of both learning accuracy and efficiency. Specifically, ML-GRL not only improves the accuracy of existing GRL models by 3.68 ~ 47.59% for link prediction and 1.75 ~ 40.90% for node classification but also significantly reduces their running time by 9.63 ~ 62.71% and 7.14 ~ 82.14%, respectively. Our source code is available at https://github.com/pnpy6elp/Multi_Level_GRL.

In this study, we propose a novel graph representation learning (GRL) model, called Two-Level GRL with Subgraph-as-a-Node (SaaN 2L-GRL in short), that partitions input graphs into smaller subgraphs for effective and scalable GRL in two levels: 1) local GRL and 2) global GRL. To realize the two-level GRL in an efficient manner, we propose an abstracted graph, called Subgraph-as-a-Node Graph (SaaN in short), to effectively maintain the high-level graph topology while significantly reducing the size of the graph. By applying the SaaN graph to both local and global GRL, SaaN 2L-GRL can effectively preserve the overall structure of the entire graph while precisely representing the nodes within each subgraph. Through time complexity analysis, we confirm that SaaN 2L-GRL significantly reduces the learning time of existing GRL models by using the SaaN graph for global GRL, instead of using the original graph, and processing local GRL on subgraphs in parallel. Our extensive experiments show that SaaN 2L-GRL outperforms existing GRL models in both accuracy and efficiency. In addition, we show the effectiveness of SaaN 2L-GRL using diverse kinds of graph partitioning methods, including five community detection algorithms and representative edge- and vertex-cut algorithms.