# Multi-View Network Representation Learning Algorithm Research
## 4 Experiments and Evaluations
### Multi-Class Classification
In addition, MVNR also can learn the network representations of the 2 nd, 3 rd and 4 th order by constructing \(k\)-step networks. DeepWalk and node2 vec belong to this kind of algorithms based on local feature acquisition. Based on DeepWalk, node2 vectakes the micro-view and macro-view of the vertices into account. Line-provides a network representation learning algorithm for various large-scale networks. Although Line-improves the training speed of network representation learning, the precision of network representation learning is not as good as DeepWalk and node2 vec. Because Line only considers first order similarity and second order similarity of the vertices. The proposed MVNR in this paper fully considers the global information of the vertices by explicitly constructing the global feature matrix. Thus, the performance of MVNR algorithm is superior to DeepWalk, node2 vec and Line. GraRep and NEU encode the features of higher order into the representation space of lower dimension. GraRep does not consider the weights of edges, it also does not consider the weights between different \(k\)-step networks. NEU only performs higher order transformation of network representation for the learnt network representations trained by other network representation learning algorithms. The MVNR algorithm proposed in this paper gives corresponding weights to the edges of different \(k\)-step networks. The larger the \(k\), the smaller the weights of the edges, which indirectly gives different weights to different \(k\)-step networks. In addition, the MVNR algorithm explicitly embeds higher order features of the network, global information and weight information into the representation space through the joint learning model. Therefore, the performance of the MVNR algorithm is better than that of GraRep, and NEU algorithms in Tables 3-5. In addition, the experimental results show that the classification performance of MVNR is better than that of Un_MVNR on Citeseer, Cora and Wiki datasets. This demonstrates that adding different weight information to different \(k\)-step networks can improve the performance of network representation learning. In fact, weight information also plays another role, namely, another feature view of the network structure. Therefore, it can also make up for the problem of sparse network structure. In Section 3.5, we theoretically discuss the time complexity of the proposed MVNR algorithm. In addition, we provide the empirical result comparing running time performance with GraRep and NEU algorithms on Citeseer dataset. First, we installed a virtual machine on a notebook computer and then we installed the MVNR running environment on that virtual machine. The notebook's memory is 8G, the processor is Core I3 2.53 GHz and the memory allocated to the virtual machine is 3G. Experimental results show that MVNR takes 15 minutes and 01 second to train the model of network representation learning, GraRep takes 5 min and 35 s to train the model of network representation learning when K is set to 6 and NEU takes about 7 seconds to convert the learnt network representations trained by DeepWalk. Although MVNR takes the longest time, the network classification performance of MVNR is superior to the GraRep and NEU algorithms on Citeseer, Cora and Wiki datasets. The main reason why MVNR become time-consuming is that MVNR uses weight matrix \(W\) to factorize the matrix \(M\) based on IMC algorithm, GraRep purely uses SVD to factorize the feature matrix of network, and NEU is just a multiplication and addition operation for the learnt representations.