# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 4 Experiments
### Effectiveness Experiments
#### 4.3.2 Clustering Task  
For the clustering task, we clustered authors based on the meta structure \(S_{2}\) and the meta paths \(p_{3}\), \(p_{4}\) in Figure 4. According to the research field, we set the number of clusters to four. Based on the similarity matrices derived by different similarity measures, we applied Normalized Cut [29] to cluster authors. We ran this 100 times for each algorithm and recorded the average accuracy.  
Table 4 demonstrates the results of different similarity measures in the clustering task, and also lists the experimental results based on the linear combination of the meta path. We can obtain the following several conclusions from Table 4. (1) StructSim outperforms StructCount, SCSE, BSCSE and MGP, which indicates that StructSim considers more rich semantics than StructCount and SCSE. (2) \(p_{3}\) has a better performance than \(p_{4}\) in the clustering task, which reveals that \(p_{3}\) can capture more semantic meaning information compared with \(p_{4}\). That is, authors who have published papers in the same venue are more likely to be classified into a cluster. (3) The linear combination of the meta path performs better than the approaches based on the meta path. This demonstrates that the linear combination can capture more comprehensive information than any single meta path.  
We also further studied how parameter \(\beta\) influenced the performance of algorithms in the entity resolution task. We varied the parameter \(\beta\) from 0 to 1 and recorded the AUC value, which is depicted in Figure 5. The experimental results on various \(\beta\) show that different weight combinations of the meta path have a significant effect on performance. With the increase in \(\beta\), the performance first gradually increases and then decreases. Algorithms achieve the best AUC value when \(\beta=0.8\), which indicates that the meta path \(p_{1}\) has a more significant impact than \(p_{2}\) in the entity resolution task. This is also consistent with our intuition. In addition, it is very meaningful to determine the proper weight of the meta path so as to achieve a better performance, which will be studied in future work.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Type**} & \multicolumn{5}{c}{**Similarity Measures**} \\ \hline \multirow{2}{*}{Meta Path} & \multicolumn{2}{c}{PathCount} & \multicolumn{2}{c}{PCRW} & \multicolumn{2}{c}{PathSim} \\ \cline{2-7}  & Mean & Dev. & Mean & Dev. & Mean & Dev. \\ \hline \(p_{3}\) & 0.361 & 0.071 & 0.529 & 0.059 & 0.650 & 0.051 \\ \(p_{4}\) & 0.181 & 0.083 & 0.367 & 0.121 & 0.391 & 0.157 \\ Linear combination & 0.361 & 0.065 & 0.529 & 0.053 & 0.651 & 0.091 \\ \hline \multirow{2}{*}{Meta structure} & \multicolumn{2}{c}{StructCount} & \multicolumn{2}{c}{SCSE} & \multicolumn{2}{c}{StructSim} \\ \cline{2-7}  & Mean & Dev. & Mean & Dev. & Mean & Dev. \\ \hline \(S\) & 0.321 & 0.062 & 0.557 & 0.058 & 0.716 & 0.029 \\ \hline Meta structure & \multicolumn{2}{c}{BSCSE} & \multicolumn{2}{c}{MGP} \\ \hline \(S\) & 0.557 & 0.028 & 0.643 & 0.059 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effectiveness results of different similarity measures in clustering task.  
Figure 5: The results of the linear combination of the meta path in the entity resolution task.