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.

翻译：图神经网络（GNN）虽为图结构数据建模开辟了光明前景，但其在设备部署中不幸带来了两大棘手障碍：（I）现有GNN多为浅层网络，主要原因是随着网络深度增加（如同卷积架构的深层化），会出现过平滑与梯度消失问题；（II）绝大多数GNN遵循同质性假设，即中心节点与其相邻节点共享相同标签。这一假设常使许多GNN在处理异质性图时面临挑战。解决上述问题已成为提升GNN应用鲁棒性和可扩展性的紧迫挑战。本文首次采用全面系统的方法攻克上述两大难题。我们提出一种名为NoSAF的节点特异性层聚合与过滤架构，该框架能够对每个节点的信息进行过滤和处理。NoSAF将"所有节点生而不等"的理念引入深度网络的每一层，旨在为每层节点提供可靠的信息过滤器，筛除对后续层无益的信息。通过引入动态更新的编码库，NoSAF能够动态优化每层向下传递的最优信息输出。这有效克服了异质性问题，同时有助于加深网络。为补偿NoSAF持续过滤导致的信息损失，我们还提出了NoSAF-D（深度版本），其在模型每一层引入补偿机制补充信息，使NoSAF即使在极深层也能进行有意义的计算。