Causal Bayesian Graphs can be constructed from causal information in text. These graphs can be sparse because the cause or effect event can be expressed in various ways to represent the same information. This sparseness can corrupt inferences made on the graph. This paper proposes to reduce sparseness by merging: equivalent nodes and their edges. This paper presents a number of experiments that evaluates the applicability of node similarity techniques to detect equivalent nodes. The experiments found that techniques that rely upon combination of node contents and structural information are the most accurate strategies, specifically we have employed: 1. node name similarity and 2.combination of node name similarity and common neighbours (SMCN). In addition, the SMCN returns "better" equivalent nodes than the string matching strategy.