Many influential areas require effective extraction and processing of graph information. Graph neural networks (GNNs) have been a type of powerful tools to obtain informative representations regarding both topology and node features. With an increasing number of graph properties being proposed and analyzed (such as homophily/heterophily, edge density, motifs, and feature distribution), numerous specific GNNs have been designed to capture them individually. However, most existing GNNs assume the entire graph shares the same property, and enforce parameter sharing across all regions of the graph. In this work, we introduce a novel class of GNNs which adopt a node-specific aggregation scheme with adaptive parameters. The node-specific parameters are generated according to node's neighborhood pattern and global position. By testing our model on semi-supervised node classification tasks on synthetic graphs and real-world benchmarks, we show its superiority over fixed-parameter models. The underlying idea could be applied as a flexible extension to different GNNs and solve a wide range of graph tasks.