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Random walks find extensive application across various complex networkdomains, including embedding generation and link prediction. Despite thewidespread utilization of random walks, the precise impact of distinct biaseson embedding generation from sequence data and their subsequent effects on linkprediction remain elusive. In this study, we conduct a comparative analysis ofseveral random walk strategies, each rooted in different biases: trueself-avoidance, unbiased randomness, bias towards node degree, and inverse nodedegree bias. Furthermore, we explore diverse adaptations of the node2vecalgorithm to induce distinct exploratory behaviors. Our empirical findingsdemonstrate 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 thespecific walk heuristic employed to traverse the network. Consequently, theresults suggest that data generated from sequences governed by unknownmechanisms can be successfully reconstructed.

Comparing biased random walks in graph embedding and link prediction