AHNA: Adaptive representation learning for attributed heterogeneous networks

随机游动 计算机科学 节点(物理) 骨料(复合) 代表(政治) 路径(计算) 嵌入 理论计算机科学 数据挖掘 人工智能 拓扑(电路) 数学 计算机网络 统计 材料科学 结构工程 组合数学 政治 政治学 法学 工程类 复合材料
作者
Lin Shu,Chuan Chen,Xingxing Xing,Xiangke Liao,Zibin Zheng
出处
期刊:International Journal of Intelligent Systems [Wiley]
卷期号:37 (2): 1157-1185 被引量:3
标识
DOI:10.1002/int.22664
摘要

Meta-path-based random walk strategy has attracted tremendous attention in heterogeneous network representation, which can capture network semantics with heterogeneous neighborhoods of nodes. Despite the success of meta-path-based random walk strategy in plain heterogeneous networks which contain no attributes, it remains unexplored how meta-path-based random walk strategy could be utilized on attributed heterogeneous networks to simultaneously capture structural heterogeneity and attribute proximity. Moreover, the importance of node attributes and structural relations generally varies across data sets, thus requiring careful considerations when they are incorporated into representations. To tackle these problems, we propose a novel method, Attributed Heterogeneous Network embedding based on Aggregate-path (AHNA), which generates aggregate-path-based random walks on attributed heterogeneous networks and adaptively fuses topological structures and node attributes based on the learned importance. Specifically, AHNA first converts node attributes to additional links in the network to deal with the heterogeneity of structures and attributes, which is followed by an adaptive random walk strategy to strike the importance balance between node attributes and topological structures, thereby generating high-quality representations. Extensive experiments are conducted on three real-world data sets, where AHNA outperforms state-of-the-art approaches by up to 22.7%, 2.6%, and 2.3% on link prediction, community detection, and node classification, respectively. Moreover, our qualitative analysis indicates that AHNA can capture different balances of topological structures and node attributes on various data sets and thus boost the quality of node representations.
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