提出三类新策略，让神经网络同时搞定有向图链接预测的三种任务。
Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
- 将有向链接预测转为多分类问题，统一处理方向与双向性。
- 在多个数据集上超越传统方法，三类子任务表现均衡提升。
- 适合需要全面评估有向图链接预测能力的研究者使用。
链接预测是图表示学习的基础任务，支持链接推荐、知识图谱补全和图生成等应用。图神经网络在该领域表现最佳，已成为处理图数据的标准方法。然而，无向与有向链接预测存在关键差异：前者仅预测边是否存在，后者还需考虑边的方向性和双向性，导致有向链接预测（DLP）包含三个子任务，取决于训练、验证和测试集的构建方式。现有研究大多忽略这一三重划分，仅关注‘存在性’子任务，即训练与测试集为随机且不相关的正负有向边样本。即便承认三重划分的工作，其模型也难以在所有子任务中表现良好。本研究实证表明，仅在存在性子任务上训练神经有向链接预测（NDLP）模型，即使经过边类别重平衡，仍无法捕捉方向性和双向性。为此，我们提出三种可同时处理三类任务的策略：第一种是多分类框架（MC-NDLP），将NDLP转化为多分类训练目标；第二种和第三种采用多任务视角，分别基于多目标（MO-DLP）或标量化解法（S-DLP）。实验结果表明，这些方法在多个数据集和模型上均优于传统方法，在三类子任务中实现相当或更优的表现。
原文摘要 · Abstract (English)
Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Neural Networks have shown the most promising results in this domain and are currently the de facto standard approach to learning from graph data. However, a key distinction exists between Undirected and Directed Link Prediction: the former just predicts the existence of an edge, while the latter must also account for edge directionality and bidirectionality. This translates to Directed Link Prediction (DLP) having three sub-tasks, each defined by how training, validation and test sets are structured. Most research on DLP overlooks this trichotomy, focusing solely on the "existence" sub-task, where training and test sets are random, uncorrelated samples of positive and negative directed edges. Even in the works that recognize the aforementioned trichotomy, models fail to perform well across all three sub-tasks. In this study, we experimentally demonstrate that training Neural DLP (NDLP) models only on the existence sub-task, using methods adapted from Neural Undirected Link Prediction, results in parameter configurations that fail to capture directionality and bidirectionality, even after rebalancing edge classes. To address this, we propose three strategies that handle the three tasks simultaneously. Our first strategy, the Multi-Class Framework for Neural Directed Link Prediction (MC-NDLP) maps NDLP to a Multi-Class training objective. The second and third approaches adopt a Multi-Task perspective, either with a Multi-Objective (MO-DLP) or a Scalarized (S-DLP) strategy. Our results show that these methods outperform traditional approaches across multiple datasets and models, achieving equivalent or superior performance in addressing the three DLP sub-tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。