Comparing biased random walks in graph embedding and link prediction

Abstract: 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.