Kavli Affiliate: Xiang Zhang
| First 5 Authors: Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Yu, Jun Huan
| Summary:
Random Walk is a basic algorithm to explore the structure of networks, which
can be used in many tasks, such as local community detection and network
embedding. Existing random walk methods are based on single networks that
contain limited information. In contrast, real data often contain entities with
different types or/and from different sources, which are comprehensive and can
be better modeled by multiple networks. To take advantage of rich information
in multiple networks and make better inferences on entities, in this study, we
propose random walk on multiple networks, RWM. RWM is flexible and supports
both multiplex networks and general multiple networks, which may form
many-to-many node mappings between networks. RWM sends a random walker on each
network to obtain the local proximity (i.e., node visiting probabilities)
w.r.t. the starting nodes. Walkers with similar visiting probabilities
reinforce each other. We theoretically analyze the convergence properties of
RWM. Two approximation methods with theoretical performance guarantees are
proposed for efficient computation. We apply RWM in link prediction, network
embedding, and local community detection. Comprehensive experiments conducted
on both synthetic and real-world datasets demonstrate the effectiveness and
efficiency of RWM.
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