Graph neural networks (GNNs) for link prediction can loosely be divided into
two broad categories. First, \emph{node-wise} architectures pre-compute
individual embeddings for each node that are later combined by a simple decoder
to make predictions. While extremely efficient at inference time (since node
embeddings are only computed once and repeatedly reused), model expressiveness
is limited such that isomorphic nodes contributing to candidate edges may not
be distinguishable, compromising accuracy. In contrast, \emph{edge-wise}
methods rely on the formation of edge-specific subgraph embeddings to enrich
the representation of pair-wise relationships, disambiguating isomorphic nodes
to improve accuracy, but with the cost of increased model complexity. To better
navigate this trade-off, we propose a novel GNN architecture whereby the
\emph{forward pass} explicitly depends on \emph{both} positive (as is typical)
and negative (unique to our approach) edges to inform more flexible, yet still
cheap node-wise embeddings. This is achieved by recasting the embeddings
themselves as minimizers of a forward-pass-specific energy function (distinct
from the actual training loss) that favors separation of positive and negative
samples. As demonstrated by extensive empirical evaluations, the resulting
architecture retains the inference speed of node-wise models, while producing
competitive accuracy with edge-wise alternatives.

图神经网络（GNNs）用于链接预测，有两种广泛的分类。首先，以节点为基础的体系结构为每个节点预先计算个体嵌入，之后由简单的解码器组合以作出预测。与推理时的极高效性相比（因为节点嵌入只计算一次并重复使用），模型的表达能力有限，即对候选边贡献的同构节点可能无法区分，从而损害准确性。相反，基于边的方法依赖于形成特定于边的子图嵌入以丰富对配对关系的表示，以消除同构节点以提高准确性，但代价是增加模型复杂性。为了更好地权衡这种权衡，我们提出了一种新颖的 GNN 体系结构，其中 “前向传递” 显式依赖于正向（一般情况下）和负向（我们方法独有）边，以摄入更灵活但仍然廉价的以节点为基础的嵌入。通过将嵌入自身重新定义为正负样本分离的正向传递特定能量函数（与实际训练损失不同），所提出的体系结构在广泛的经验评估中得到了验证，既保持了以节点为基础的模型的推理速度，又产生了与基于边的替代方法竞争性的准确性。