# Multi-View Network Representation Learning Algorithm Research
## 2 Related Work  
Network representation learning aims to embed the features of the network to representation spaces of lower dimension. The network features are mainly composed of structural features, text features of nodes, community attributes and labels, which is also the input of network representation learning. Network representation learning is similar to network coding task. The output of network representation learning is representation vector space, which makes some nodes with similar features have a closer distance in vector space. Most network analytics approaches possess higher computation complexity. Network representation learning is an effective yet efficient method to deal with the network analytics challenges, which converts the network data into the representation space of lower dimension in which the network structural features and properties are maximumly preserved. Network representation learning benefits various network analytics tasks as the vector representations, the learnt representations also can be applied efficiently in both time and space. For example, we can conduct the node classification, node clustering, node recommendation, node retrieval, node ranking, link prediction and network visualization and so forth. There also exist other example scenarios, such as, multimedia network embedding, Information propagation and social networks alignment and so forth. According to the application whether the network properties are applied to modeling procedure or not, the existing network representation learning algorithms are mainly divided into two kind of different research systems. The first kind of research system is only based on the network structural features and the second kind of research system is based on the joint learning model. Their representative algorithms are DeepWalk [1] and TriDNR [6], respectively. The methods based on network structural features aim to explore and research the performance improvement of network representation learning based on the structure features of the network. For example, [PERSON] et al. [1] proposed the DeepWalk algorithm in 2014. DeepWalk [1] adopts the Skip-Gram model in Word2 Vec [9], modifying the input of the model from (Current Word, Context Words) pairs into (Current Vertex, Context Vertices) pairs. These pairs are inputted to a shadow neural network, which can fully embed the structure features into representation spaces of lower dimension. Consequently, DeepWalk has been successfully applied to various tasks [10]. [PERSON] [8] proposed the first-order and second-order proximities function for learning large-scale network representation in 2015. Node2 vec [7] improves random walk procedure of DeepWalk to sample neighboring vertices around the current vertex by introducing breadth-first search (BFS) and depth-first search (DFS) methods. Structural Deep Network Embedding (SDNE) [11] integrates global information during random walk procedure for network representation learning. [PERSON] et al. [12] introduces the polysemy idea to representation learning which trains different network representations for current vertex according to different context nodes. There also exist some network representation approaches for the special network structures, such as DynamicTriad [13], DepthLGP [14] and Structural Deep Embedding for Hyper-Networks (DHNE) [15] and so forth. In addition, motivated by Generative Adversarial Networks (GAN) [16], some network representation learning algorithms introduce GAN to optimize the learning procedure, such as ANE [17], GraphGAN [18] and NetGan [19]. The method is based on the joint learning model and mainly improves the performance of network representation learning by means of introducing other attribute information of the network, such as community information, text contents and labels and so forth. According to existing literature findings, the performance of the joint network representation learning algorithm is better than that of the algorithm using only network structure information.