In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm.

在本文中，我们关注于在基于事件的社交网络（EBSN）中发现内部连接的社区的问题，并提出一种利用用户之间的社交影响的社区检测方法。与传统的社交网络不同，EBSN包含不同类型的实体和链接，并且EBSN中的用户具有更复杂的行为。这导致传统的社会影响力计算方法在EBSN中表现不佳。因此，为了准确地量化EBSN中的成对社会影响力，我们首先建议利用在线社交网络结构和用户的离线社交行为来计算两种类型的社会影响力，即基于结构的社会影响力和基于行为的社会影响力。 。特别是，根据EBSN的特定功能，用户偏好在三个方面（即主题，区域和组织者）的相似性被用来衡量基于行为的社会影响力。然后，我们通过权重函数将这两种类型的社会影响结合起来，从而获得统一的成对社会影响。接下来，我们提出一种基于社会影响力的社区检测算法，称为SICD。在SICD中，受自动编码器非线性特征学习能力的启发，我们首先设计了一种基于邻域的深度自动编码器算法，以获取面向用户的非线性面向社区的潜在表示，然后利用 接下来，我们提出一种基于社会影响力的社区检测算法，称为SICD。在SICD中，受自动编码器非线性特征学习能力的启发，我们首先设计了一种基于邻域的深度自动编码器算法，以获取面向用户的非线性面向社区的潜在表示，然后利用 接下来，我们提出一种基于社会影响力的社区检测算法，称为SICD。在SICD中，受自动编码器非线性特征学习能力的启发，我们首先设计了一种基于邻域的深度自动编码器算法，以获取面向用户的非线性面向社区的潜在表示，然后利用k均值算法用于社区检测。在现实世界的数据集上进行的实验结果证明了我们提出的算法的有效性。