计算机工程

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一种保留社区结构信息的网络嵌入算法

  

  • 发布日期:2020-12-17

A Network Embedding Algorithm with Preserving Community Information

  • Published:2020-12-17

摘要: 现有网络嵌入算法大多只保留了网络的微观结构信息,忽略了网络中普遍存在的社区结构信息,为了将社区结构信 息融入到网络嵌入中,以提高网络表示的质量,本文提出了一种保留社区结构信息的网络嵌入算法 PCNE(Preserving Community information in Network Embedding)。首先通过最大化节点之间的一阶和二阶相似性对网络的微观结构进行建模, 然后通过分解能够反映网络社区结构信息的社区结构嵌入矩阵对网络的社区结构信息进行建模,最后将二者融合到统一的联 合非负矩阵分解框架中,通过微观结构的相似度矩阵和中观结构的社区隶属度矩阵的共同指导得到了融合社区结构信息的节 点表示向量。本文在五个真实公开数据集上进行了节点分类实验,实验结果显示 PCNE 比其他现有五种算法分别提升了 Micro-F1 值 0.96%~13.1%,从而验证了本算法的有效性。

Abstract: Most existing network embedding algorithms only retain the micro-structure information of the network, but ignore the community structure information which is important in networks. In order to incorporate the community structure information into the network embedding to improve the quality of network representation, a network embedding algorithm PCNE (Preserving Community information in Network Embedding) that preserves community structure information was proposed. First, the micro-structure of the network is modeled by maximizing the first-order and second-order similarity between nodes, and then the community structure information of the network is modeled by factorizing the community structure embedding matrix which can reflect the community structure information of the network. Finally, under the joint supervision of the similarity matrix of the micro-structure and the community membership matrix of the meso-structure, PCNE obtains the node representation vectors that fused the community structure information by merging the both into a unified joint non-negative matrix factorization framework. We evaluate the performance of the PCNE algorithm by node classification experiments on five real public datasets and compared with other five state-of-the-art embedding models. Experimental results show that the proposed method improves the Micro-F1 by 0.96% ~ 13.1% compared with the other algorithms, thus verifying the effectiveness of PCNE.