Fig 1.
On the left figure, an example of a decision sapling illustrating the relationship between two firms, A and T, in a scenario featuring 100 technologies.
The node at the bottom indicates the percentage of technologies that are (right) and are not (left) connected to firm A; in this figure, A is connected to 20% of technologies, as denoted by the green value. The upper nodes show how these percentages change when considering only technologies connected to Firm T (right node) or only those not connected to Firm T (left node). On the right figure, we show the value of each box as a function of the number of co-occurrences CO, the degrees k, and the total number of technologies N.
Fig 2.
Performance of the various Sapling Similarity variants in predicting M&A deals in the three cases: pair, target, and acquirer prediction.
The variants, reported on the x-axis, are described in the Methods section. We report in pink the case in which the input network binary, and in green, the weighted case. The MASS algorithm corresponds to the green SS(1+2) variant. The red line shows the performance of cosine similarity measured using the weighted network. All improvements enhance the prediction performances, MASS being the outperforming algorithm.
Fig 3.
Comparative analysis of MASS and LightGCN performances across different M&A prediction scenarios.
The top row of radar plots presents the performance metrics for both methods when the test is conducted on the entire dataset of 547 M&As. Here, MASS outperforms. The bottom row depicts the same exercises, but the test focuses exclusively on the subset of 123 M&As between companies with zero co-occurrences (0-CO). In this case, LightGCN is able to capture the hidden, higher-order similarities between companies.