An Implementation of Standard Label Propagation Algorithm
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This article presents an implementation of the standard label propagation algorithm, structured as a callable function for immediate use. The algorithm is particularly valuable for social network analysis and recommendation systems, operating as a graph-based semi-supervised learning method. It functions by propagating labels from labeled nodes to unlabeled nodes through iterative updates across the graph structure. The implementation typically involves constructing an affinity matrix to represent node similarities, followed by iterative label propagation until convergence criteria are met. Key functions include neighborhood similarity calculation and label update rules that ensure consistency across connected nodes. Through this implementation, developers can better understand and apply semi-supervised learning concepts while gaining deeper insights into the fundamental mechanics of social networks and recommendation systems.
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