图排序：通过学习实现最优
Graph Ordering: Towards the Optimal by Learning
摘要：
图表示学习在节点分类、链接预测、社区发现等许多基于图的应用中取得了显著的成功。这些模型通常被设计为在不同的粒度上保留顶点信息，并将离散空间中的问题简化为连续空间中的一些机器学习任务。然而，尽管取得了丰硕的成果，但对于某些图的应用，如图压缩和边划分，很难将其归结为一些图的表示学习任务。此外，这些问题是密切相关的重新制定一个特定的图，这是一个重要的NP-难的组合优化问题的全局布局：图排序。在本文中，我们提出了一种新的学习方法来攻击这些应用程序背后的图排序问题。与基于预定义规则的贪心算法不同，本文提出了一种神经网络模型：深度有序网络（Deep Order Network，DON），用于从部分顶点序集中捕捉隐藏的局部结构。在抽样偏序的监督下，DON具有推断未知组合的能力。此外，为了缓解DON训练空间的组合爆炸问题，实现高效的偏序采样，我们采用了一种强化学习模型--策略网络，在DON训练阶段自动调整偏序采样概率。为此，政策网络可以提高培训效率，并引导DON向更有效的模型自动进化。综合实验的合成和真实数据验证，DON-RL优于目前最先进的启发式算法一致。两个关于图压缩和边划分的案例研究证明了DON-RL在实际应用中的潜在能力。
英文摘要：
Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, link prediction, and community detection. These models are usually designed to preserve the vertex information at different granularity and reduce the problems in discrete space to some machine learning tasks in continuous space. However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. Moreover, these problems are closely related to reformulating a global layout for a specific graph, which is an important NP-hard combinatorial optimization problem: graph ordering. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach. Distinguished from greedy algorithms based on predefined heuristics, we propose a neural network model: Deep Order Network (DON) to capture the hidden locality structure from partial vertex order sets. Supervised by sampled partial order, DON has the ability to infer unseen combinations. Furthermore, to alleviate the combinatorial explosion in the training space of DON and make the efficient partial vertex order sampling , we employ a reinforcement learning model: the Policy Network, to adjust the partial order sampling probabilities during the training phase of DON automatically. To this end, the Policy Network can improve the training efficiency and guide DON to evolve towards a more effective model automatically. Comprehensive experiments on both synthetic and real data validate that DON-RL outperforms the current state-of-the-art heuristic algorithm consistently. Two case studies on graph compression and edge partitioning demonstrate the potential power of DON-RL in real applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
