Attributed Network Embedding for Incomplete Attributed Networks
This addresses a practical issue for network analysis in real-world scenarios where data is often incomplete, though it is incremental as it builds on existing attributed network embedding methods.
The paper tackles the problem of learning node embeddings for incomplete attributed networks with missing links or attributes, proposing a method that reconstructs a denser network by fusing structural and attribute information and uses random walks for embedding, achieving validated effectiveness on six real-world datasets.
Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and attribute information. The resulting node embeddings can then facilitate various network downstream tasks e.g. link prediction. Although there are several ANE methods, most of them cannot deal with incomplete attributed networks with missing links and/or missing node attributes, which often occur in real-world scenarios. To address this issue, we propose a robust ANE method, the general idea of which is to reconstruct a unified denser network by fusing two sources of information for information enhancement, and then employ a random walks based network embedding method for learning node embeddings. The experiments of link prediction, node classification, visualization, and parameter sensitivity analysis on six real-world datasets validate the effectiveness of our method to incomplete attributed networks.