Community aware random walk for network embedding

Abstract

Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named "CARE" for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE.We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.

The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

“…MNMF [10] successfully saves the community structure in the generated node representations and improves the quality of node representation vectors, it preserves the first and second-order similarities of nodes with Non-negative Matrix Factorization(NMF), models community structure with modularity maximization. CARE [11] uses community attributes to capture more network structure information. It designs a community-aware random walk strategy and uses the Skip-gram model to learn the node representations.…”

“…For example, MNMF [10] preserves the community structure of the network while considering the proximity of nodes. CARE [11] designs the community-aware random walks when learning the node representation vectors. Based on Expansion Sampling(XS) strategy [12] [13] [14], SENE [15] saves the mesoscopic community information in the learned node representation vectors.…”

DeepLink: A novel link prediction framework based on deep learning

“…The Hadamard operator is the best operator for edge feature learning from user feature vectors [24], [26]. An edge feature vector indicates that the source and destination nodes of a link are similar based on their structural and content features.…”

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

“…In machine learning applications, the traditional single label classification (SLC) problem has been explored substantially. However, more recently, the multi-label classification (MLC) problem has attracted increasing research interest because of its wide range of applications, such as text classification [ 1 , 2 ], social network analysis [ 3 ], gene function classification [ 4 ], and image/video annotation [ 5 ]. With SLC, one instance only belongs to one category, whereas with MLC, it can be allocated to multiple categories simultaneously.…”