Network embedding aims at projecting the network data into a low-dimensional feature space, where the nodes are represented as a unique feature vector and network structure can be effectively preserved. In recent years, more and more online application service sites can be represented as massive and complex networks, which are extremely challenging for traditional machine learning algorithms to deal with. Effective embedding of the complex network data into low-dimension feature representation can both save data storage space and enable traditional machine learning algorithms applicable to handle the network data. Network embedding performance will degrade greatly if the networks are of a sparse structure, like the emerging networks with few connections. In this paper, we propose to learn the embedding representation for a target emerging network based on the broad learning setting, where the emerging network is aligned with other external mature networks at the same time. To solve the problem, a new embedding framework, namely "Deep alIgned autoencoder based eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link and attribute in a unified analytic based on broad learning, and introduces the multiple aligned attributed heterogeneous social network concept to model the network structure. A set of meta paths are introduced in the paper, which define various kinds of connections among users via the heterogeneous link and attribute information. The closeness among users in the networks are defined as the meta proximity scores, which will be fed into DIME to learn the embedding vectors of users in the emerging network. Extensive experiments have been done on real-world aligned social networks, which have demonstrated the effectiveness of DIME in learning the emerging network embedding vectors.
Link prediction and recommendation first proposed in @cite_15 has become a very important problem in online social networks, which provides social network researchers with the opportunity to study both the network properties from the individuals social connection perspective. Traditional unsupervised link predictor proposed in @cite_15 mainly calculate the closeness scores among users, and assume that close users tend to be friends in the network. @cite_9 is the first to study the link prediction problem as a supervised learning problem, where the existing and non-existing social links are treated as the positive and negative instances respectively. Today, many social networks are heterogeneous and to conduct the link prediction in these networks, @cite_16 propose a meta path-based prediction model to predict co-author relationship in the heterogeneous bibliographic network.
@cite_9 @cite_15 @cite_16
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Social network analysis has attracted much attention in recent years. Link prediction is a key research directions within this area. In this research, we study link prediction as a supervised learning task. Along the way, we identify a set of features that are key to the superior performance under the supervised learning setup. The identified features are very easy to compute, and at the same time surprisingly effective in solving the link prediction problem. We also explain the effectiveness of the features from their class density distribution. Then we compare different classes of supervised learning algorithms in terms of their prediction performance using various performance metrics, such as accuracy, precision-recall, F-values, squared error etc. with a 5-fold cross validation. Our results on two practical social network datasets shows that most of the well-known classification algorithms (decision tree, k-nn,multilayer perceptron, SVM, rbf network) can predict link with surpassing performances, but SVM defeats all of them with narrow margin in all different performance measures. Again, ranking of features with popular feature ranking algorithms shows that a small subset of features always plays a significant role in the link prediction job. Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, .e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects ( .g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called, .e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.