Recent advancements in heterogeneous GNNs have enabled significant progress in embedding nodes and learning relationships across diverse tasks. However, traditional methods rely heavily on meta-paths grounded in node types, which often fail to encapsulate the full complexity of node interactions, leading to inconsistent performance and elevated computational demands. To address these challenges, we introduce MF2Vec, a novel framework that shifts focus from rigid node-type dependencies to dynamically exploring shared facets across nodes, regardless of type. MF2Vec constructs multi-faceted paths and forms homogeneous networks to learn node embeddings more effectively. Through extensive experiments, we demonstrate that MF2Vec achieves superior performance in node classification, link prediction, and node clustering tasks, surpassing existing baselines. Furthermore, it exhibits reduced performance variability due to meta-path dependencies and achieves faster training convergence. These results highlight its capability to analyze complex networks comprehensively. The implementation of MF2Vec is publicly available at https://github.com/kimjongwoo-cell/MF2Vec.

The 15-minute city (15MC) is an urban planning concept that promotes sustainable and inclusive cities where residents can access essential services within a short amount of time (i.e., 15 minutes). However, evaluating 15MC compliance in hyper-dense cities remains challenging due to: (1) traditional manual assessments that are resource-intensive and difficult to scale, and (2) POI-based metrics that suffer from data unavailability and lack of spatial contexts. In this paper, we propose a novel evaluation framework that directly assesses 15MC compliance from geospatial imagery in three stages: image pre-processing, representation learning, and instance aggregation. To validate the framework, we have constructed a new dataset of 2,794 residential areas in Seoul, pairing high-resolution geospatial imagery with functional urban labels. Furthermore, we have developed a model, GeoTwin-MIL, on the basis of the proposed framework. The model includes two key components: (1) cross-modal contrastive learning that aligns satellite and map representations to capture both morphological (building density) and topological (road networks) features, enabling robust inference using only satellite images, and (2) multiple instance learning to efficiently aggregate geospatial details while detecting localized urban functions within high-resolution imagery. The experimental results obtained from various evaluation settings show that GeoTwin-MIL significantly outperforms single-modality approaches or vision baselines, validating the integrative effectiveness of the two key components and supporting the transferability of the model without POI dependencies. The code is available at https://github.com/20243439/geotwin_mil.git.