iWalk: Interest-Aware Random Walk for Network Embedding

Network embedding plays a key role in network analysis, due to its ability to represent features of network structure in a low-dimensional Euclidean space, making it possible to directly utilize the of f-the-shelf mining techniques in a variety of analysis tasks. Although fruitful research papers on network embedding have sprung up in recent years, most of them neglect an important fact that nodes and edges in real-world networks are of diverse interests especially when the network contains little side information such as labels. To tackle this challenge, we propose a novel iWalk model to learn interest-aware network embedding in an unsupervised fashion. iWalk can automatically assign interest to nodes and edges based on network topology and construct custom paths navigated by assigned interest, then Skip-gram is used to learn network embedding from these paths. Sufficient experiments are conducted on different tasks and three typical datasets, the empirical results demonstrate that our model outperform the stat-of-art methods in most instances.