多网络上的随机游走
Random Walk on Multiple Networks
AI总结：
本文提出多网络上的随机游走方法 RWM，通过多个游走者相互强化来融合多网络信息，并给出理论收敛分析与高效近似算法，在链路预测、网络嵌入和局部社区检测中验证了其有效性和效率。
摘要：
随机游走是探索网络结构的基本算法，可用于许多任务，例如局部社区检测和网络嵌入。现有的随机游走方法基于包含有限信息的单一网络。相比之下，真实数据通常包含具有不同类型或/和来自不同来源的实体，这些实体是全面的，并且可以通过多网络更好地建模。为了利用多网络中的丰富信息并对实体做出更好的推断，在本研究中，我们提出了多网络上的随机游走，即 RWM。RWM 是灵活的，同时支持多重网络和一般多网络，这些网络之间可能形成多对多的节点映射。RWM 在每个网络上发送一个随机游走者，以获得相对于起始节点的局部邻近性（即节点访问概率）。具有相似访问概率的游走者相互强化。我们从理论上分析了 RWM 的收敛性质。提出了两种具有理论性能保证的近似方法以实现高效计算。我们将 RWM 应用于链路预测、网络嵌入和局部社区检测。在合成数据集和真实世界数据集上进行的综合实验证明了 RWM 的有效性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
