# Multi-View Network Representation Learning Algorithm Research
## 5 Conclusions  
In this paper, we propose a unified network representation learning framework (MVNR) which can embed hider order neighboring relations and global information into the representation space of a lower dimension. In addition, we adopt the weight matrix factor to balance different \(k\)-step network representations with the modeling procedure of the joint representation learning, namely, we adopt the inductive matrix completion algorithm for synchronously learning the network features from both the network structures and link weights. Empirically, the learnt representations can be effectively applied to various machine learning tasks, such as clustering, classification, visualization and link prediction and so forth. The experimental results show that the proposed MVNR can effectively capture the global features and higher order relations between vertices at the same time. Meanwhile, the classification performance of MVRN outperforms the popular global network representation learning algorithms (Line) and higher order network representation learning algorithms (GraRep and NEU). Our Future works would explore the extendibility of our algorithm in other representation learning tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Dataset** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Ratio** \\ \end{tabular} } & \multicolumn{8}{c}{**Algorithm Name**} & \multicolumn{8}{c}{**MVNR**} \\ \cline{3-13}  & & & **CN** & **LP** & **RA** & **ACT** & **LHNII** & **Cos+** & **LRW** & **SRW** & \
\begin{tabular}{c} **(K = 3)** \\ \end{tabular} &
\begin{tabular}{c} **(K = 6)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Citeseer} & 0.7 & 68.13 & 81.06 & 66.37 & 75.88 & 95.76 & 88.57 & 87.21 & 86.34 & 95.15 & 93.96 \\  & 0.8 & 72.08 & 86.83 & 72.12 & 75.59 & 96.85 & 89.38 & 90.13 & 90.05 & 95.70 & 96.22 \\  & 0.9 & 74.67 & 88.45 & 74.63 & 73.79 & 96.20 & 88.49 & 91.25 & 90.47 & 97.08 & 97.01 \\ \hline \multirow{3}{*}{Cora} & 0.7 & 69.50 & 80.12 & 69.47 & 74.11 & 89.41 & 90.25 & 88.48 & 88.40 & 92.09 & 92.60 \\  & 0.8 & 72.38 & 82.97 & 72.47 & 73.67 & 90.37 & 90.98 & 90.58 & 90.50 & 93.15 & 92.47 \\  & 0.9 & 78.19 & 87.90 & 77.97 & 74.00 & 93.64 & 93.22 & 93.63 & 93.62 & 94.42 & 93.43 \\ \hline \multirow{3}{*}{Wiki} & 0.7 & 85.79 & 92.60 & 85.99 & 80.19 & 86.45 & 91.49 & 93.65 & 93.48 & 93.38 & 93.66 \\  & 0.8 & 87.95 & 93.56 & 88.24 & 80.35 & 86.77 & 91.63 & 94.20 & 94.18 & 93.95 & 93.44 \\ \cline{1-1}  & 0.9 & 90.47 & 94.00 & 90.33 & 79.98 & 87.68 & 92.71  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant Number: 61763041, 11661069 and 61663041), the Program for Changjiang Scholars and Innovative Research Team in Universities (Grant Number: IRT_15R40) and the Fundamental Research Funds for the Central Universities, China (Grant Number: 2017 TS045).  
**Acknowledgments:** The authors wish to express their gratitude to the anonymous reviewers and the associate editors for their rigorous comments during the review process. In addition, the authors also would like to thank experimenters in our laboratory for their great contribution to data-collection work. They are [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.