Convolutional neural networks (CNNs) provide a dramatically powerful class of models, but are subject to traditional
convolution that can merely aggregate permutation-ordered and dimension-equal local inputs. It causes that CNNs are allowed to
only manage signals on Euclidean or grid-like domains (e.g., images), not ones on non-Euclidean or graph domains (e.g., traffic
networks). To eliminate this limitation, we develop a local-aggregation function, a sharable nonlinear operation, to aggregate
permutation-unordered and dimension-unequal local inputs on non-Euclidean domains. In the context of the function
approximation theory, the local-aggregation function is parameterized with a group of orthonormal polynomials in an effective and
efficient manner. By replacing the traditional convolution in CNNs with the parameterized local-aggregation function,
Local-Aggregation Graph Networks (LAGNs) are readily established, which enable to fit nonlinear functions without activation
functions and can be expediently trained with the standard back-propagation. Extensive experiments on various datasets strongly
demonstrate the effectiveness and efficiency of LAGNs, leading to superior performance on numerous pattern recognition and
machine learning tasks, including text categorization, molecular activity detection, taxi flow prediction, and image classification.