图关系感知的持续学习
Graph Relation Aware Continual Learning
AI总结：
针对持续图学习中知识迁移与分布偏移难题，本文提出关系感知自适应模型 RAM-CG，通过关系发现和任务掩码建模潜在图关系，并在三个数据集上显著超越现有方法。
摘要：
持续图学习（Continual graph learning，CGL）研究从无限的图数据流中学习、巩固历史知识并将其泛化到未来任务的问题。在任一时刻，只有当前图数据可用。尽管近期已有一些尝试来处理这一任务，但我们仍面临两个潜在挑战：1）现有大多数工作只对中间图嵌入进行操作，而忽视了图的内在属性。要区分跨图迁移的信息并非易事。2）近期尝试采用参数共享策略在不同时间步之间迁移知识，或者在图分布发生偏移时逐步扩展新架构。学习单一模型可能会丢失每个图任务的判别信息，而模型扩展方案则面临较高的模型复杂度。在本文中，我们指出图边背后的潜在关系可归因于演化图中的一个不变因素，并且潜在关系的统计信息会不断演化。受此启发，我们设计了一种关系感知的自适应模型，称为 RAM-CG，该模型由一个用于挖掘边背后潜在关系的关系发现模块，以及一个用于应对分布偏移的任务感知掩码分类器组成。大量实验表明，RAM-CG 在 CitationNet、OGBN-arxiv 和 TWITCH 数据集上分别比当前最先进结果带来显著的 2.2%、6.9% 和 6.6% 的准确率提升。
英文摘要：
Continual graph learning (CGL) studies the problem of learning from an infinite stream of graph data, consolidating historical knowledge, and generalizing it to the future task. At once, only current graph data are available. Although some recent attempts have been made to handle this task, we still face two potential challenges: 1) most of existing works only manipulate on the intermediate graph embedding and ignore intrinsic properties of graphs. It is non-trivial to differentiate the transferred information across graphs. 2) recent attempts take a parameter-sharing policy to transfer knowledge across time steps or progressively expand new architecture given shifted graph distribution. Learning a single model could loss discriminative information for each graph task while the model expansion scheme suffers from high model complexity. In this paper, we point out that latent relations behind graph edges can be attributed as an invariant factor for the evolving graphs and the statistical information of latent relations evolves. Motivated by this, we design a relation-aware adaptive model, dubbed as RAM-CG, that consists of a relation-discovery modular to explore latent relations behind edges and a task-awareness masking classifier to accounts for the shifted. Extensive experiments show that RAM-CG provides significant 2.2%, 6.9% and 6.6% accuracy improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and TWITCH dataset, respective.
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