Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.

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Con&Net：一种基于嵌入表示的跨网络锚链接发现方法

跨网络锚链接发现是一个重要的研究问题，在异构社交网络中有很多应用。现有的跨网络锚链接发现方案可以提供合理的链接发现结果，但这些结果的质量取决于平台的特性。因此，稳定性没有理论上的保证。本文采用用户嵌入特征对跨平台账户之间的关系进行建模，即用户嵌入特征越相似，两个账户就越相似。用户嵌入特征的相似性由用户特征在潜在空间中的距离决定。基于用户嵌入特征，本文提出了一种基于嵌入表示的方法Con& Net（内容和网络）解决跨网络锚链接发现问题。Con&Net 结合用户的个人资料特征、用户生成内容 (UGC) 特征和用户的社会结构特征来衡量两个用户帐户的相似度。Con&Net 首先训练用户的个人资料特征以获得个人资料嵌入。然后它训练节点的网络结构以获得结构嵌入。它通过向量连接将两个特征连接起来，并根据嵌入向量计算向量的余弦相似度。该余弦相似度用于衡量用户帐户的相似度。最后，Con&Net 根据两个网络中帐户对的相似性预测链接。在新浪微博和推特网络上的大量实验表明，所提出的方法Con& Net 优于最先进的方法。锚链接预测的接受者操作特征（ROC）曲线的曲线下面积（AUC）值比基线方法高11%，Precision@30比基线方法高25%。