GAGE: Geometry Preserving Attributed Graph Embeddings
GAGE: Geometry Preserving Attributed Graph Embeddings
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DOI：
10.1145/3488560.3498467
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Charilaos I. Kanatsoulis;N. Sidiropoulos
中科院分区：
文献类型：
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作者：
Charilaos I. Kanatsoulis;N. Sidiropoulos
节点嵌入是提取网络中连接的某些实体的简明和信息表示的任务。各种真实世界的网络都以特征或时间序列数据的形式包含有关节点连通性和某些节点属性的信息。现代表示学习技术利用节点的连通性和属性信息来以无监督的方式产生嵌入。在这种情况下，导出保持网络几何和属性向量的嵌入将是非常理想的，因为它们将反映特征空间中的拓扑邻域结构和邻近性。虽然当只观察网络的连通性或属性信息时，保持这一点相当简单，但保留这两种信息的几何形状是具有挑战性的。提出了一种新的属性网络中节点嵌入的张量分解方法，该方法保持了连接和属性之间的距离。此外，还开发了一种有效的轻量级算法来处理学习任务，多个最新基线的明智实验表明，所提出的算法在下游任务中具有显著的性能改进。
Node embedding is the task of extracting concise and informative representations of certain entities that are connected in a network. Various real-world networks include information about both node connectivity and certain node attributes, in the form of features or time-series data. Modern representation learning techniques employ both the connectivity and attribute information of the nodes to produce embeddings in an unsupervised manner. In this context, deriving embeddings that preserve the geometry of the network and the attribute vectors would be highly desirable, as they would reflect both the topological neighborhood structure and proximity in feature space. While this is fairly straightforward to maintain when only observing the connectivity or attribute information of the network, preserving the geometry of both types of information is challenging. A novel tensor factorization approach for node embedding in attributed networks is proposed in this paper, that preserves the distances of both the connections and the attributes. Furthermore, an effective and lightweight algorithm is developed to tackle the learning task and judicious experiments with multiple state-of-the-art baselines suggest that the proposed algorithm offers significant performance improvements in downstream tasks.