Aributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. Œe learned embeddings could advance various learning tasks such as node classi€cation, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure o‰en evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their aŠribute values are also naturally changing, with the emerging of new content paŠerns and the fading of old content paŠerns. Œese changing characteristics motivate us to seek an effective embedding representation to capture network and aŠribute evolving paŠerns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the €rst to tackle this problem with the following two challenges: (1) the inherently correlated network and node aŠributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic aŠributed network embedding framework DANE. In particular, DANE €rst provides an o„ine method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real aŠributed networks to corroborate the e‚ectiveness and eciency of the proposed framework.