In a speaker content graph, vertices represent speech signals and edges represent speaker similarity. Link prediction methods calculate which potential edges are most likely to connect vertices from the same speaker; those edges are included in the generated speaker content graph. Since a variety of speaker recognition tasks can be performed on a content graph, we provide a set of metrics for evaluating the graph's quality independently of any recognition task. We then describe novel global and incremental algorithms for constructing accurate speaker content graphs that outperform the existing k nearest neighbors link prediction method. We evaluate those algorithms on a NIST speaker recognition corpus.