对比两种分布式学习算法在不同网络下的表现，发现多路径随机游走更优。

Although gossip and random walk-based learning algorithms are widely known for decentralized learning, there has been limited theoretical and experimental analysis to understand their relative performance for different graph topologies and data heterogeneity. We first design and analyze a random walk-based learning algorithm with multiple streams (walks), which we name asynchronous "Multi-Walk (MW)". We provide a convergence analysis for MW w.r.t iteration (computation), wall-clock time, and communication. We also present a convergence analysis for "Asynchronous Gossip", noting the lack of a comprehensive analysis of its convergence, along with the computation and communication overhead, in the literature. Our results show that MW has better convergence in terms of iterations as compared to Asynchronous Gossip in graphs with large diameters (e.g., cycles), while its relative performance, as compared to Asynchronous Gossip, depends on the number of walks and the data heterogeneity in graphs with small diameters (e.g., complete graphs). In wall-clock time analysis, we observe a linear speed-up with the number of walks and nodes in MW and Asynchronous Gossip, respectively. Finally, we show that MW outperforms Asynchronous Gossip in communication overhead, except in small-diameter topologies with extreme data heterogeneity. These results highlight the effectiveness of each algorithm in different graph topologies and data heterogeneity. Our codes are available for reproducibility.