Graph Pooling by Local Cluster Selection
This work addresses graph pooling for researchers and practitioners in graph-based machine learning, presenting an incremental advancement in graph shrinking techniques.
The paper tackles the problem of graph pooling by introducing a novel procedure and a node-centered operator for shrinking graphs within Graph Neural Networks, aiming to improve the efficiency and effectiveness of graph processing.
Graph pooling is a family of operations which take graphs as input and produce shrinked graphs as output. Modern graph pooling methods are trainable and, in general inserted in Graph Neural Networks (GNNs) architectures as graph shrinking operators along the (deep) processing pipeline. This work proposes a novel procedure for pooling graphs, along with a node-centred graph pooling operator.