发表机构
Chongqing University of Posts and Telecommunications; School of Computer Science and Technology, Chongqing University of Posts and Telecommunications; Chongqing Ant ConsumerFinance Co,. Ltd, Ant Group; Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education; Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing（重庆邮电大学; 重庆邮电大学计算机科学与技术学院; 蚂蚁集团重庆蚂蚁消费金融有限公司; 教育部计算智能重点实验室、大数据智能计算重点实验室、网络空间大数据智能安全重点实验室; 重庆计算智能重点实验室、重庆大数据智能计算重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有链接预测方法忽略图同质性结构多粒度特性的问题，提出MGLP方法，通过自适应粒球图细化与多粒度层级距离编码生成多粒度位置嵌入，在链接预测任务中表现优异。
AI 中文摘要
链接预测旨在识别给定图结构中潜在或未来的连接。位置信息对链接预测至关重要，它通过相对关系区分同质节点，助力准确捕捉结构模式与隐式连接。现有研究将节点位置信息推导为到单粒度地标（定义为同质性区域的中心）的距离，却忽略了同质性结构的多粒度特性及其层级关联。我们提出用于链接预测的基于粒球的图多粒度位置嵌入方法（MGLP），以获取图的多粒度位置嵌入。具体而言，MGLP引入自适应粒球图细化机制，将图自适应细化为具有最优粒度水平的同质子域；子域内的中心节点被视为地标，形成层级中心图。此外，我们提出一种新颖的多粒度层级距离编码机制，以同时捕捉图内的同质性结构及其层级关联，提升节点的判别能力。实验结果表明，与链接预测的基线算法相比，本方法生成的多粒度位置嵌入表现出优异性能和强竞争力。我们的代码可在该https URL获取。
英文摘要
Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogeneous nodes through their relative relationships, facilitating the accurate capture of structural patterns and implicit connections. Previous studies derive node positional information as distances to single-granularity landmarks, defined as the centers of homophilic regions, while neglecting the multi-granularity nature of homophilic structures and their hierarchical interrelations. We propose the Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction (MGLP) method to obtain multi-granularity position embedding of graphs. Specifically, MGLP introduces an Adaptive Granular-Ball Graph Refinement mechanism to adaptively refine the graph into homophilic subdomains with optimal levels of granularity. The central nodes within subdomains are treated as landmarks, which form a Hierarchical Central Graph. Moreover, a novel Multi-granularity Hierarchical Distance encoding mechanism is proposed to capture both the homophilic structures within a graph and their hierarchical correlations, improving the discriminative power of nodes. Experimental results demonstrate that the multi-granularity position embedding generated by our method exhibits excellent performance and strong competitiveness compared to baseline algorithms for link prediction. Our codes are available in https://anonymous.4open.science/r/MGLP-D3C5/.
