# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Multi-Class Classification
On the other hand, we use the NEU algorithm to transform the learnt network representations generated by DeepWalk algorithm into higher order one but the performance of the network representations of higher order is inferior than that of the proposed MVNR algorithm in network classification task. The main reason is that GraRep ignores the weight information between different \(k\)-step networks and it also neglects the edge weights in each \(k\)-step network. Importantly, MVNR gives corresponding weight value to edges of each \(k\)-step network, which can indirectly affect the weights between different \(k\)-step networks. NEU is not involved in the learning procedure of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**\%Labeled Nodes** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline DeepWalk & 73.29 & 75.46 & 76.19 & 77.49 & 77.89 & 77.83 & 78.86 & 79.05 & 78.62 \\ Line & 65.13 & 70.17 & 72.20 & 72.92 & 73.45 & 75.67 & 75.25 & 76.78 & 79.34 \\ node2 vec & 76.30 & 79.26 & 80.43 & 80.70 & 81.13 & 81.26 & 82.18 & 81.63 & 82.81 \\ WMF (\(K=1\)) & 68.77 & 71.75 & 72.53 & 73.68 & 73.71 & 74.54 & 74.56 & 75.04 & 75.55 \\ NEU & 73.21 & 76.23 & 77.49 & 78.25 & 78.89 & 79.94 & 80.85 & 81.00 & 81.04 \\ \hline GraRep (\(K=1\)) & 63.92 & 72.51 & 74.85 & 75.92 & 76.80 & 76.74 & 76.85 & 77.19 & 77.92 \\ GraRep (\(K=3\)) & 72.60 & 77.34 & 78.34 & 79.39 & 79.43 & 80.30 & 80.32 & 80.68 & 79.88 \\ GraRep (\(K=6\)) & 75.95 & 78.54 & 79.64 & 80.40 & 80.70 & 81.42 & 81.74 & 81.36 & 82.92 \\ \hline Un\_MVNR (\(K=1\)) & 70.43 & 76.97 & 78.61 & 79.63 & 80.68 & 80.31 & 80.16 & 81.60 & 82.25 \\ Un\_MVNR (\(K=3\)) & 75.32 & 79.18 & 80.06 & 80.88 & 81.44 & 81.62 & 82.46 & 82.25 & 83.59 \\ Un\_MVNR (\(K=6\)) & 76.15 & 78.52 & 79.98 & 80.65 & 81.70 & 82.35 & 82.98 & 81.97 & 82.59 \\ \hline MVNR (\(K=1\)) & 74.96 & 77.86 & 78.69 & 80.04 & 80.26 & 80.69 & 80.91 & 80.99 & 81.81 \\ MVNR (\(K=3\)) & 77.50 & 79.69 & 81.15 & 81.42 & 82.38 & 82.86 & 83.05 & 83.79 & 82.78 \\ MVNR (\(K=6\)) & 78.24 & 80.44 & 81.08 & 82.08 & 82.29 & 83.12 & 83.25 & 83.90 & 83.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy (%) of vertex classification on Cora. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**\%Labeled Nodes** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline DeepWalk & 52.15 & 58.79 & 61.69 & 62.80 & 62.92 & 63.05 & 64.38 & 64.25 & 64.41 \\ Line & 51.17 & 53.62 & 57.81 & 57.26 & 58.94 & 62.46 & 62.24 & 66.74 & 67.35 \\ node2 vec & 57.51 & 61.11 & 63.17 & 63.73 & 64.39 & 65.40 & 65.58 & 65.37 & 66.50 \\ WMF (\(K=1\)) & 35.22 & 39.68 & 40.92 & 42.71 & 42.97 & 43.95 & 46.04 & 44.31 & 44.87 \\ NEU & 52.03 & 58.36 & 60.08 & 61.95 & 62.98 & 63.90 & 63.16 & 64.56 & 64.08 \\ \hline GraRep (\(K=1\)) & 55.50 & 59.24 & 60.40 & 61.30 & 61.43 & 62.11 & 63.28 & 61.08 & 62.95 \\ GraRep (\(K=3\)) & 55.98 & 61.15 & 62.79 & 63.75 & 64.21 & 65.24 & 65.20 & 65.08 & 65.54 \\ GraRep (\(K=6\)) & 56.73 & 60.42 & 62.77 & 64.43 & 65.42 & 65.44 & 65.96 & 66.97 & 66.37 \\ \hline Un\_MVNR (\(K=1\)) & 57.22 & 61.88 & 63.19 & 65.68 & 65.03 & 66.11 & 66.61 & 65.89 & 68.91 \\ Un\_MVNR (\(K=3\)) & 55.63 & 60.84 & 63.76 & 64.65 & 65.41 & 66.07 & 67.11 & 66.70 & 67.70 \\ Un\_MVNR (\(K=6\)) & 54.72 & 60.49 & 62.31 & 63.63 & 64.40 & 65.41 & 65.65 & 66.91 & 67.95 \\ \hline MVNR (\(K=1\)) & 57.74 & 62.27 & 64.26 & 64.51 & 65.81 & 66.50 & 66.69 & 67.93 & 68.33 \\ MVNR (\(K=3\)) & 58.29 & 61.98 & 63.81 & 65.56 & 66.08 & 66.54 & 66.62 & 67.92 & 68.08 \\ MVNR (\(K=6\)) & 57.55 & 62.37 & 63.99 &high-order network representation, and NEU is only a high-order transformation of the network representations trained by other network representation algorithms. NEU [6] is essentially different from GraRep and MVNR. MVNR can embed the global information into lower dimension embeddings by explicitly constructing the global feature matrix of the network.