# A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
## 5 Experiments
### Results and Analysis  
#### 5.3.1 Classification of Nodes  
In our experiments, we use classical method-accuracy to verify the effectiveness of MCHIN. We perform experiments on three real-world datasets--DBLP, Yelp, and IMDb. For more convincing results, we randomly select \(3\%,5\%,7\%,10\%\) labeled objects as the priori knowledge and classify the rest of the data in the DBLP dataset. Because Yelp dataset is very sparser than DBLP, we randomly select the proportion of labeled objects as \(x\%(\text{where }x=10,30,50\text{ and }70)\). In the IMDB dataset, we choose the ratio of labeled objects \(x\%(\text{where }x=10,30,50\text{ and }70)\) similar to the Yelp dataset. In our work, \(\lambda_{ij}\) indicates the selection of the important types of links during the ranking process. As discussed in Reference [17], we set \(\alpha_{i}=0.1,\lambda_{ij}=0.2\). They are good enough to verify the validity of MCHIN. The results are shown in Tables 3-5, and the highest performance is in bold. In DBLP, clearly, when the proportion of labeled nodes are only \(3\%\), the accuracy of MCHIN can be up to \(87.6\%\). That outperforms Deepwalk more than \(18\%\), and better than the best baselines models by \(7\%\). By increasing the proportion of labeled objects, the performance of the proposed MCHIN has been far ahead than other comparison methods. The accuracy of MCHIN can achieve \(90.5\%\) by labeling \(10\%\) of the objects. In fact, our proposed model MCHIN is significantly more effective than MCHIN-ori especially when there is a lower number of labels, as MCHIN uses extension meta graphs. In Table 4, our method consistently outperforms the other baselines on the Yelp dataset as well. The results of LLGC, wvRN, RankClass and Deepwalk methods are quite similar to each other. The reasons are two-folds: (1) the four baseline methods are generally applied to homogeneous information networks. They lose the quality semantics and the useful complex information when they are applied to HINs. (2) None of the baseline approaches consider extra-label information through different types of models in HINs. When extending the meta graphs, MCHIN augments the prior label sets. This improves the performance 1% more than the MCHIN-ori method on average. From Table 5, we can see that the effectiveness of MCHIN is higher than the best performance obtained by the baseline methods from 3% to 8% when the labeled objects change from 30% to 90%. The LLGC, wvRN, Deepwalk, and SDNE are generally used in homogeneous information networks, none of them can capture the rich semantic information in HINs. As the results of LLGC, wvRN, Deepwalk and SDNE methods show, they are significantly lower than others. Both of RankClass and HIN2 Vec algorithms are applied in the HINs, they can gain more semantic information in different relationships. Using meta paths in HIN2 Vec effectively improve accuracy. However, the performance of MCHIN-ori is still up to 8% more than HIN2 Vec because of adding meta graphs when the labeled objects are 70%. Furthermore, when we extend the meta graphs, the accuracy increases by 3% when the labeled objects are 50%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **\% Labeled-Nodes** & **30\%** & **50\%** & **70\%** & **90\%** \\ \hline \multirow{8}{*}{Yelp} & LLGC & 0.312 & 0.35 & 0.36 & 0.379 \\  & wvRN & 0.296 & 0.354 & 0.359 & 0.365 \\  & RankClass & 0.324 & 0.36 & 0.362 & 0.383 \\  & Deepwalk & 0.349 & 0.368 & 0.367 & 0.389 \\  & HIN2 Vec & 0.360 & 0.382 & 0.396 & 0.411 \\  & SDNE & 0.374 & 0.389 & 0.396 & 0.415 \\  & MCHIN-ori & 0.363 & 0.403 & 0.409 & 0.416 \\  & MCHIN & **0.382** & **0.408** & **0.414** & **0.428** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification of nodes on Yelp dataset. The highest performance is in bold.