  The ever-designed Graph Neural Networks, though opening a promising path for
the modeling of the graph-structure data, unfortunately introduce two daunting
obstacles to their deployment on devices. (I) Most of existing GNNs are
shallow, due mostly to the over-smoothing and gradient-vanish problem as they
go deeper as convolutional architectures. (II) The vast majority of GNNs adhere
to the homophily assumption, where the central node and its adjacent nodes
share the same label. This assumption often poses challenges for many GNNs
working with heterophilic graphs. Addressing the aforementioned issue has
become a looming challenge in enhancing the robustness and scalability of GNN
applications. In this paper, we take a comprehensive and systematic approach to
overcoming the two aforementioned challenges for the first time. We propose a
Node-Specific Layer Aggregation and Filtration architecture, termed NoSAF, a
framework capable of filtering and processing information from each individual
nodes. NoSAF introduces the concept of "All Nodes are Created Not Equal" into
every layer of deep networks, aiming to provide a reliable information filter
for each layer's nodes to sieve out information beneficial for the subsequent
layer. By incorporating a dynamically updated codebank, NoSAF dynamically
optimizes the optimal information outputted downwards at each layer. This
effectively overcomes heterophilic issues and aids in deepening the network. To
compensate for the information loss caused by the continuous filtering in
NoSAF, we also propose NoSAF-D (Deep), which incorporates a compensation
mechanism that replenishes information in every layer of the model, allowing
NoSAF to perform meaningful computations even in very deep layers.
