In this paper, we study the effects of different node and edge weighting strategies of graph-based semantic representations on the accuracy of a scholarly paper recommendation scenario. Our semantic representation relies on the use of Knowledge Graphs (KGs) for acquiring relevant additional information about concepts and their semantic relations, thus resulting in a knowledge-rich graph document model. Recent studies have used this representation as the basis of a scholarly paper recommendation system. Even when the recommendation is made based on the comparison of graphs, little has been explored regarding the effects of the weights assigned to the edges and nodes in the representation. In this paper, we present the initial results obtained from a comparative study of the effects of different weighting strategies on the quality of the recommendations. Three weighting strategies for edges (Number of Paths (NP), Semantic Connectivity Score (SCS), and Hierarchical Similarity (HS)) and two for nodes (Concept Frequency (CF) and PageRank (PR)) are considered. Results show that the combination of the SCS and CF outperform the other weighting strategy combinations and the considered baselines.