# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Multi-Class Classification  
In this experiment, we first learn the network representations based on MVNR algorithm. Based on the learnt representations, we then train a Support Vector Machine (SVM) classifier with different proportions of training sets. For the training set, we let training proportions of dataset vary from 10% to 90%. The remaining data is regarded as the testing set. We repeat the procedure for 10 times and report the average accuracy value. We set the \(K\) value as 1, 3 and 6. For example, we concatenate the network representations of the 1-step, 2-step and 3-step networks when \(K\) is 3. The detailed results are shown in Tables 3-5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Random** & **Random** & **Representation** & **The Number** & **Context** \\  & **Walk Length** & **Walk Number** & **Vector Size** & **of Orders** & **Window Size** \\ \hline DeepWalk & 80 & 10 & 200 & - & 5 \\ \hline Line & - & - & 200 & 2 & 5 \\ \hline node2 vec & 80 & 10 & 200 & - & 5 \\ \hline NEU & 80 & 10 & 200 & - & 5 \\ \hline GraRep & - & - & 100 for \(k\)-th & 1,3,6 & - \\ \hline WMF & - & - & 100 & 1 & - \\ \hline MVNR & - & - & 100 for \(k\)-th & 1,3,6 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**\%Labeled Nodes** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline DeepWalk & 48.31 & 50.36 & 51.33 & 52.31 & 52.85 & 53.33 & 52.98 & 53.47 & 53.71 \\ Line & 39.82 & 46.83 & 49.02 & 50.65 & 53.77 & 54.20 & 53.87 & 54.67 & 53.82 \\ node2 vec & 54.38 & 57.29 & 58.64 & 59.53 & 59.63 & 59.88 & 60.43 & 61.36 & 62.42 \\ WMF (\(K=1\)) & 52.14 & 54.17 & 55.38 & 55.42 & 56.41 & 56.57 & 57.23 & 57.79 & 57.22 \\ NEU & 50.23 & 52.00 & 53.62 & 54.07 & 54.46 & 53.74 & 54.34 & 55.51 & 55.29 \\ \hline GraRep (\(K=1\)) & 26.21 & 34.13 & 38.30 & 40.82 & 41.92 & 44.68 & 44.29 & 45.30 & 44.83 \\ GraRep (\(K=3\)) & 45.07 & 50.95 & 53.40 & 54.21 & 54.87 & 55.75 & 55.54 & 55.15 & 54.22 \\ GraRep (\(K=6\)) & 50.72 & 53.02 & 54.21 & 55.22 & 55.51 & 56.17 & 55.99 & 55.78 & 57.94 \\ \hline Un\_MVNR (\(K=1\)) & 51.07 & 55.65 & 56.96 & 57.84 & 58.04 & 58.30 & 58.95 & 58.21 & 58.76 \\ Un\_MVNR (\(K=3\)) & 54.54 & 58.36 & 59.17 & 60.90 & 61.43 & 61.76 & 61.67 & 62.38 & 62.32 \\ Un\_MVNR (\(K=6\)) & 54.90 & 59.23 & 60.31 & 61.12 & 61.96 & 62.47 & 62.49 & 63.15 & 62.14 \\ \hline MVNR (\(K=1\)) & 54.32 & 56.67 & 57.99 & 58.94 & 59.26 & 59.59 & 60.11 & 59.89 & 60.39 \\ MVNR (\(K=3\)) & 56.99 & 59.80 & 61.57 & 62.54 & 63.73 & 64.71 & 64.93 & 64.98 & 66.22 \\ MVNR (\(K=6\)) & 57.63 & 60.61 & 61.39 & 63.69 & 64.15 & 65.05 & 65.59 & 65.97 & 66.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy (%) of vertex classification on Citeseer. In classification evaluation, MVNR consistently and significantly improves the accuracy of diverse network representations on three evaluation datasets. In addition, MVNR outperforms the baseline algorithms and showing its excellent performance with diverse training ratios on Citeseer, Cora and Wiki datasets. Obviously, the performance of higher order network representation learning (GraRep, NEU and MVNR) is better than that of the network representation learning algorithm based on local feature acquisition (DeepWalk, Line and node2 vec). In this classification evaluation, we set \(K\) as 1, 3 and 6, which means that we learn and concatenate the network representations of the 1-step, {1, 2, 3}-step and {1, 2, 3, 4, 5, 6}-step networks for GraRep and MVNR. The experimental results show that although GraRep can capture neighboring relations of higher order, MVNR appears to capture better neighboring vertices of higher order than GraRep on three datasets.