Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to all words or attributes. In this paper, we argue that considering topic-level semantic interactions between nodes is crucial to learn discriminative node embedding vectors. In order to model pairwise topic relevance between linked text nodes, we propose topical network embedding, where interactions between nodes are built on the shared latent topics. Accordingly, we propose a unified optimization framework to simultaneously learn topic and node representations from the network text contents and structures, respectively. Meanwhile, the structure modeling takes the learned topic representations as conditional context under the principle that two nodes can infer each other contingent on the shared latent topics. Experiments on three real-world datasets demonstrate that our approach can learn significantly better network representations, i.e., 4.1% improvement over the state-of-the-art methods in terms of Micro-F1 on Cora dataset. (The source code of the proposed method is available through the github link: https://github.com/codeshareabc/TopicalNE.)

中文翻译：

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主题网络嵌入

网络数据涉及来自多渠道的复杂信息，包括拓扑结构，节点内容和/或节点标签等，其中结构和内容通常是相关的，但并不总是一致的。典型的情况是学术出版物中的引文关系，其中论文被他人引用不是因为它们具有相同的内容，而是因为它们共享一个或多个主题。迄今为止，尽管存在许多网络嵌入方法来考虑节点内容，但它们都将节点内容视为简单的平面字/属性集，并且假定共享连接的节点相对于所有字或属性都具有依赖性。在本文中，我们认为考虑节点之间的主题级语义交互对于学习判别性节点嵌入向量至关重要。为了对链接的文本节点之间的成对主题相关性建模，我们提出了主题网络嵌入，其中节点之间的交互作用建立在共享的潜在主题上。因此，我们提出了一个统一的优化框架，以同时从网络文本内容和结构中同时学习主题和节点表示。同时，结构建模在两个节点可以根据共享的潜在主题相互推断的原则下，将学习到的主题表示作为条件上下文。在三个真实数据集上的实验表明，我们的方法可以学习到更好的网络表示，即在Cora数据集上的Micro-F1方面比最新方法提高4.1％。（建议的方法的源代码可通过github链接获取：https：