基于元路径的异构DBLP网络挖掘中的网络嵌入
On Applying Meta-path for Network Embedding in Mining Heterogeneous DBLP Network
摘要：
近年来，网络嵌入在挖掘现实世界信息网络中的应用已被广泛报道。大多数信息网络在本质上是异构的。元路径是异构网络嵌入生成的常用方法之一。由于元路径将模型引导到特定的子结构，它往往会丢失底层网络中固有的一些异构特性。在本文中，我们系统地研究了不同的元路径的影响，使用不同的国家的最先进的网络嵌入方法（元路径2vec，节点2vec，和VERSE）在DBLP书目网络和评估嵌入的性能使用两个应用程序（合著预测和作者研究领域分类任务）。从各种实验观察，很明显，使用不同的元路径嵌入在不同的任务上表现不同。这表明Meta路径是任务相关的，不能推广到不同的任务。我们进一步观察到，在考虑书目网络中所有节点和关系类型后获得的嵌入优于基于Meta路径的嵌入。
英文摘要：
In recent time, applications of network embedding in mining real-world information network have been widely reported in the literature. Majority of the information networks are heterogeneous in nature. Meta-path is one of the popularly used approaches for generating embedding in heterogeneous networks. As meta-path guides the models towards a specific sub-structure, it tends to lose some hetero- geneous characteristics inherently present in the underlying network. In this paper, we systematically study the effects of different meta-paths using different state-of-art network embedding methods (Metapath2vec, Node2vec, and VERSE) over DBLP bibliographic network and evaluate the performance of embeddings using two applications (co-authorship prediction and authors research area classification tasks). From various experimental observations, it is evident that embedding using different meta-paths perform differently over different tasks. It shows that meta- paths are task-dependent and can not be generalized for different tasks. We further observe that embedding obtained after considering all the node and relation types in bibliographic network outperforms its meta- path based counterparts.
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