Link prediction can help rectify inaccuracies in various graph algorithms, stemming from unaccounted-for or overlooked links within networks. However, many existing works use a baseline approach, which incurs unnecessary computational costs due to its high time complexity. Further, many studies focus on smaller graphs, which can lead to misleading conclusions. This submission introduces our parallel approach, called LHub, which predict links using neighborhood-based similarity measures on large graphs. LHub is a heuristic approach that disregards large hubs, based on the idea that high-degree nodes contribute little similarity among their neighbors. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, LHub is on average \(1019\times \) faster than not disregarding hubs, especially on web graphs and social networks, while maintaining similar prediction accuracy. Notably, LHub achieves a link prediction rate of 38.1M edges/s and improves performance at a rate of \(1.6\times \) for every doubling of threads.