Graph representation learning (GRL) is to encode graph elements into
informative vector representations, which can be used in downstream tasks for
analyzing graph-structured data and has seen extensive applications in various
domains. However, the majority of extant studies on GRL are geared towards
generating node representations, which cannot be readily employed to perform
edge-based analytics tasks in edge-attributed bipartite graphs (EABGs) that
pervade the real world, e.g., spam review detection in customer-product reviews
and identifying fraudulent transactions in user-merchant networks. Compared to
node-wise GRL, learning edge representations (ERL) on such graphs is
challenging due to the need to incorporate the structure and attribute
semantics from the perspective of edges while considering the separate
influence of two heterogeneous node sets U and V in bipartite graphs. To our
knowledge, despite its importance, limited research has been devoted to this
frontier, and existing workarounds all suffer from sub-par results.
Motivated by this, this paper designs EAGLE, an effective ERL method for
EABGs. Building on an in-depth and rigorous theoretical analysis, we propose
the factorized feature propagation (FFP) scheme for edge representations with
adequate incorporation of long-range dependencies of edges/features without
incurring tremendous computation overheads. We further ameliorate FFP as a
dual-view FFP by taking into account the influences from nodes in U and V
severally in ERL. Extensive experiments on 5 real datasets showcase the
effectiveness of the proposed EAGLE models in semi-supervised edge
classification tasks. In particular, EAGLE can attain a considerable gain of at
most 38.11% in AP and 1.86% in AUC when compared to the best baselines.

本文设计了一种名为 EAGLE 方法的有效边缘表示学习方法，针对边缘属性二部图 (Edge-Attributed Bipartite Graphs) 中的边缘分析任务，通过因式特征传播 (Factorized Feature Propagation) 方案加以改进，充分考虑到来自 U 和 V 两个异构节点集的影响，实现了边缘表示的准确性和计算效率的提升。通过对 5 个真实数据集的广泛实验，验证了所提出的 EAGLE 模型在半监督边缘分类任务中的有效性，与最佳基准模型相比，EAGLE 模型可以在平均准确率 (AP) 方面提高最多 38.11%，在曲线下面积 (AUC) 方面提高 1.86%。