Selection of store sites is a common but challenging task in business practices. Picking the most desirable location for a future store is crucial for attracting customers and becoming profitable. The classic multi-criteria decision-making framework for store site selection oversimplifies the local characteristics that are both high dimensional and unstructured. Recent advances in deep learning enable more powerful data-driven approaches for site selection, many of which, however, overlook the interaction between different locations on the map. To better incorporate the spatial interaction patterns in understanding neighborhood characteristics and their impact on store placement, we propose to learn a graph convolutional network (GCN) for highly effective site selection tasks. Furthermore, we present a novel dataset that encompasses land use information as well as public transport networks in Singapore as a case study to benchmark site selection algorithms. It allows us to construct a geospatial GCN based on the public transport system to predict the attractiveness of different store sites within neighborhoods. We show that the proposed GCN model outperforms the competing methods that are learning from local geographical characteristics only. The proposed case study corroborates the geospatial interactions and offers new insights for solving various geographic and transport problems using graph neural networks.
Recent advances in Graph Neural Networks (GNNs) have achieved superior results in many challenging tasks, such as few-shot learning. Despite its capacity to learn and generalize a model from only a few annotated samples, GNN is limited in scalability, as deep GNN models usually suffer from severe over-fitting and over-smoothing. In this work, we propose a novel GNN framework with a triple-attention mechanism, i.e., node self-attention, neighbor attention, and layer memory attention, to tackle these challenges. We provide both theoretical analysis and illustrations to explain why the proposed attentive modules can improve GNN scalability for few-shot learning tasks. Our experiments show that the proposed Attentive GNN model outperforms the state-of-the-art few-shot learning methods using both GNN and non-GNN approaches. The improvement is consistent over the mini-ImageNet, tiered-ImageNet, CUB-200-2011, and Flowers-102 benchmarks, using both ConvNet-4 and ResNet-12 backbones, and under both the inductive and transductive settings. Furthermore, we demonstrate the superiority of our method for few-shot fine-grained and semi-supervised classification tasks with extensive experiments. The code for this work is publicly available at https://github.com/chenghao-ch94/AGNN.
Highlights d Some bronchiectasis patients exhibit increased airway abundance of Neisseria spp. d The culturable species N. subflava weakens barrier integrity and induces inflammation d N. subflava elicits distinct transcriptomic/metabolipidomic signatures in the mouse lung d Neisseria-associated pathogenic signatures are observed in bronchiectasis patients Authors
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