Degree-biased random walk for large-scale network embedding
Yunyi Zhang (Huazhong University of Science and Technology)
Zhan Shi (Huazhong University of Science and Technology)
Dan Feng (Huazhong University of Science and Technology)
Xiuxiu Zhan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Network embedding aims at learning node representation by preserving the network topology. Previous embedding methods do not scale for large real-world networks which usually contain millions of nodes. They generally adopt a one-size-fits-all strategy to collect information, resulting in a large amount of redundancy. In this paper, we propose DiaRW, a scalable network embedding method based on a degree-biased random walk with variable length to sample context information for learning. Our walk strategy can well adapt to the scale-free feature of real-world networks and extract information from them with much less redundancy. In addition, our method can greatly reduce the size of context information, which is efficient for large-scale network embedding. Empirical experiments on node classification and link prediction prove not only the effectiveness but also the efficiency of DiaRW on a variety of real-world networks. Our algorithm is able to learn the network representations with millions of nodes and edges in hours on a single machine, which is tenfold faster than previous methods.