Message passing Graph Neural Networks (GNNs) provide a powerful modeling
framework for relational data. However, the expressive power of existing GNNs
is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test,
which means GNNs that are not able to predict node clustering coefficients and
shortest path distances, and cannot differentiate between different d-regular
graphs. Here we develop a class of message passing GNNs, named Identity-aware
Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL
test. ID-GNN offers a minimal but powerful solution to limitations of existing
GNNs. ID-GNN extends existing GNN architectures by inductively considering
nodes' identities during message passing. To embed a given node, ID-GNN first
extracts the ego network centered at the node, then conducts rounds of
heterogeneous message passing, where different sets of parameters are applied
to the center node than to other surrounding nodes in the ego network. We
further propose a simplified but faster version of ID-GNN that injects node
identity information as augmented node features. Altogether, both versions of
ID-GNN represent general extensions of message passing GNNs, where experiments
show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy
improvement on challenging node, edge, and graph property prediction tasks; 3%
accuracy improvement on node and graph classification benchmarks; and 15% ROC
AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs
demonstrate improved or comparable performance over other task-specific graph
networks.

本文提出了一种名为 Identity-aware Graph Neural Networks (ID-GNNs) 的消息传递图神经网络，通过节点中心的次元网络和异构消息传递来考虑节点的身份，在图形预测任务中比传统的 GNNs 提供更高的准确性。

身份感知图神经网络

Identity-aware Graph Neural Networks

Computing shortest path distances between nodes lies at the heart of many
graph algorithms and applications. Traditional exact methods such as
breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving
today's massive networks. Therefore, it is required to find approximation
methods to enable scalable graph processing with a significant speedup. In this
paper, we utilize vector embeddings learnt by deep learning techniques to
approximate the shortest paths distances in large graphs. We show that a
feedforward neural network fed with embeddings can approximate distances with
relatively low distortion error. The suggested method is evaluated on the
Facebook, BlogCatalog, Youtube and Flickr social networks.

本文提出使用深度学习中的向量嵌入来近似计算大型网络的最短路径距离，结果表明，使用向量嵌入的前馈神经网络可以比较低的失真误差实现距离计算。该方法在 Facebook、BlogCatalog、Youtube 和 Flickr 等社交网络上进行了评估。