二值化属性网络嵌入

Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang
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引用次数: 71

摘要

属性网络嵌入实现了节点链接和属性的联合表示学习。现有的属性网络嵌入模型是在连续欧氏空间中设计的,往往会引入数据冗余,给存储和计算成本带来挑战。为此,我们提出了一种二值化属性网络嵌入模型(简称BANE)来学习二值节点表示。具体而言,我们定义了一个新的Weisfeiler-Lehman接近矩阵,通过分层方式聚合节点属性信息和从相邻节点到给定目标节点的链接信息来捕获节点链路和属性之间的数据依赖关系。基于Weisfiler-Lehman邻近矩阵,在二元节点表示约束下,构造了一个新的Weisfiler-Lehman矩阵分解学习函数。该学习问题是一个混合整数优化问题,并采用了一种高效的循环坐标下降(CCD)算法作为解决方案。在实际数据集上进行的节点分类和链路预测实验表明,贝恩模型优于目前最先进的网络嵌入方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Binarized attributed network embedding
Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.
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