论文标题
利用元路径上下文进行异质信息网络中的分类
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks
论文作者
论文摘要
异构信息网络（HIN）具有不同类型的顶点对象，并且可以边缘对象之间的关系，这些对象也是各种类型的。我们研究将对象分类为HIN的问题。当将标记为稀缺的物体作为训练集时，大多数现有方法的性能很差，并且在这种情况下提高分类精度的方法通常在计算上通常很昂贵。为了解决这些问题，我们提出了图形神经网络模型Conch。 Conch将分类问题提出为多任务学习问题，将半监督学习与自我监督的学习结合在一起，以从标记和未标记的数据中学习。 Conch采用元路径，它们是捕获对象之间语义关系的对象类型的序列。 CONCH共同介绍对象嵌入和上下文嵌入通过图卷积。它还使用注意机制将这种嵌入融合。我们进行了广泛的实验，以评估其他15种分类方法的海螺的性能。我们的结果表明，海螺是HIN分类的有效方法。
A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types. We study the problem of classifying objects in HINs. Most existing methods perform poorly when given scarce labeled objects as training sets, and methods that improve classification accuracy under such scenarios are often computationally expensive. To address these problems, we propose ConCH, a graph neural network model. ConCH formulates the classification problem as a multi-task learning problem that combines semi-supervised learning with self-supervised learning to learn from both labeled and unlabeled data. ConCH employs meta-paths, which are sequences of object types that capture semantic relationships between objects. ConCH co-derives object embeddings and context embeddings via graph convolution. It also uses the attention mechanism to fuse such embeddings. We conduct extensive experiments to evaluate the performance of ConCH against other 15 classification methods. Our results show that ConCH is an effective and efficient method for HIN classification.
