  Random walks find extensive application across various complex network
domains, including embedding generation and link prediction. Despite the
widespread utilization of random walks, the precise impact of distinct biases
on embedding generation from sequence data and their subsequent effects on link
prediction remain elusive. In this study, we conduct a comparative analysis of
several random walk strategies, each rooted in different biases: true
self-avoidance, unbiased randomness, bias towards node degree, and inverse node
degree bias. Furthermore, we explore diverse adaptations of the node2vec
algorithm to induce distinct exploratory behaviors. Our empirical findings
demonstrate that despite the varied behaviors inherent in these embeddings,
only slight performance differences manifest in the context of link prediction.
This implies the resilient recovery of network structure, regardless of the
specific walk heuristic employed to traverse the network. Consequently, the
results suggest that data generated from sequences governed by unknown
mechanisms can be successfully reconstructed.
