# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 5 Experiments
### Experimental Results  
**Accuracy. ** We assessed the efficacy of our method by comparing its accuracy with various other methods. To ensure fairness in the experiments, we maintained consistent links in the test set. For discerning accuracy across different scales, we segmented the Aminer dataset into subsets: Aminer-10000, Aminer-5000, Aminer-2000, and Aminer-1000, \begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Formulas** \\ \hline Aminer & _FieldinA_\((x,y)\longrightarrow\)_Citedby_\((x,y)\) \\  & _AffiliationinA_\((x,y)\longrightarrow\)_Coauthor_\((x,y)\) \\  & _FieldinA_\((x,y)\longrightarrow\)_Citedby_\((x,y)\) \\  & (_Coauthor_\((x,y)\wedge\)_Coauthor_\((y,z))\longrightarrow\)_Coauthor_\((x,z)\) \\  & (_Citedby_\((x,y)\wedge\)_Citedby_\((y,z))\longrightarrow\)_Citedby_\((x,z)\) \\ UMLS & _Affects_\((x,y)\wedge\)_Affects_\((y,z)\wedge\)_Affects_\((x,z)\) \\  & _Affects_\((x,y)\wedge\)_Causes_\((x,y)\) \\ Kinship & _Father_\((x,y)\wedge\)_Male_\((x)\wedge\)_Son_\((x,y)\) \\  & _Mother_\((x,y)\wedge\)_Male_\((x)\wedge\)_Son_\((x,y)\) \\  & _Father_\((x,y)\wedge\)_Father_\((x,z)\wedge\)_Male_\((y)\wedge\)_Male_\((z)\wedge\)_Brother_\((y,z)\) \\ FB15k-237 & _Position_\((x,y)\wedge\)_Position_\((y,z)\longrightarrow\)_Position_\((x,z)\) \\  & _Cermony_\((x,y)\wedge\)_Cermony_\((y,z)\longrightarrow\)_CategoryOf_\((x,z)\) \\  & _Film_\((x,y)\wedge\)_Film_\((y,z)\longrightarrow\)_Participant_\((x,z)\) \\  & _Stoy_\((x,y)\longrightarrow\)_Participant_\((x,y)\) \\  & _Adjoins_\((x,y)\wedge\)_Country_\((y,z)\longrightarrow\)_ServiceLocation_\((x,z)\) \\ WN18 RR & _Member_\(\_\)_eronym_\((x,y)\wedge\)_Hypernym_\((x,y)\) \\  & _Similar_\({}_{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_} {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_} {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_} {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_} {\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}} \\ \end{tabular}
\end{table} 
Table 4: Formulas. containing 12,601, 3591, 2384, and 1189 relations, respectively. Two types of links were designated for prediction, with an 8/2 ratio between training and testing data. Furthermore, we examined the outcomes of combinations with varying numbers of link types and ratios, specifically (3 types, 8/2) and (2 types, 7/3), to analyze the impact of these factors on results. The accuracy comparisons are detailed and summarized in Tables 5-7. In cases where a model failed to operate due to memory constraints, this is denoted by '-'. Our findings suggest the following:  
* Across 2-type and 3-type links at different train-test ratios, our method attains an average accuracy of 94.87%, which stands as the highest accuracy across all dataset sizes. Notably, our method enhances accuracy by approximately 3.7%, 2.9%, 0.5%, 6.0%, and 11.5% compared to the highest accuracy achieved by the comparison methods, respectively. * Across varying sizes of the Aminer datasets, our method consistently surpasses all competitors, showcasing enhanced robustness. Notably, our method boosts accuracy by 3.7%, 3.1%, 5.3%, and 1.8% compared to the second-highest performing model on Aminer with 10,000, 5000, 2000, and 1000 individuals, respectively. * The MLN-based methods (ExpressGNN, MLN, PlogicNet, Pgat, RNNlogic, and our method) attain average accuracies of 85.71%, 82.81%, 41.52%, 34.4%, and 72.66% on the Aminer, Kinship, UMLS, WN18 RR, and FB15k-237 datasets, respectively, surpassing the learning-based methods (SEAL and MF) by 8.3%, 0.1%, 171.55%, 140.1%, and 6.2%, respectively.