矩阵完成
嵌入
计算机科学
利用
正多边形
代表(政治)
基质(化学分析)
理论计算机科学
矩阵表示法
节点(物理)
诱导子图同构问题
图形
秩(图论)
算法
拓扑(电路)
人工智能
数学
组合数学
折线图
材料科学
法学
化学
有机化学
几何学
群(周期表)
高斯分布
复合材料
工程类
物理
政治
结构工程
量子力学
计算机安全
电压图
政治学
作者
Zhu Cao,Linlin Wang,Gerard de Melo
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2018-04-29
卷期号:32 (1)
被引量:17
标识
DOI:10.1609/aaai.v32i1.11655
摘要
Link prediction is of fundamental importance in network science and machine learning. Early methods consider only simple topological features, while subsequent supervised approaches typically rely on human-labeled data and feature engineering. In this work, we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology. In contrast to the SGNS or SVD methods espoused in previous representation-based studies, our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank, and thereby, more effectively eliminate noise in the representation. Thus, subgraph embeddings and convex matrix completion are elegantly integrated into a novel link prediction framework. Experimental results on several datasets show the effectiveness of our method compared to previous work.
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