Link Prediction Based on Common-Neighbors for Dynamic Social Network

AbstractLink prediction is an important issue in social networks. Most of the existing methods aim to predict interactions between individuals for static networks, ignoring the dynamic feature of social networks. This paper proposes a link prediction method which considers the dynamic topology of social networks. Given a snapshot of a social network at time t (or network evolution between t1 and t2), we seek to accurately predict the edges that will be added during the interval from time t (or t2) to a given future time t′. Our approach utilizes three metrics, the time-varied weight, the change degree of common neighbor and the intimacy between common neighbors. Moreover, we redefine the common neighbors by finding them within two hops. Experiments on DBLP show that our method can reach better results

Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective

Understanding mechanisms driving link formation in dynamic social networks is a long-standing problem that has implications to understanding social structure as well as link prediction and recommendation. Social networks exhibit a high degree of transitivity, which explains the successes of common neighbor-based methods for link prediction. In this paper, we examine mechanisms behind link formation from the perspective of an ego node. We introduce the notion of personalized degree for each neighbor node of the ego, which is the number of other neighbors a particular neighbor is connected to. From empirical analyses on four on-line social network datasets, we find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. This is complementary to the finding of Adamic and Adar that neighbor nodes with higher (global) degree are less likely to lead to new link formations. Furthermore, on directed networks, we find that personalized out-degree has a stronger effect on link formation than personalized in-degree, whereas global in-degree has a stronger effect than global out-degree. We validate our empirical findings through several link recommendation experiments and observe that incorporating both personalized and global degree into link recommendation greatly improves accuracy.Comment: To appear at the 10th International Workshop on Modeling Social Media co-located with the Web Conference 201

A model to predict communications in dynamic social networks

Background: social networks are dynamic due to continuous increases in their members, communications, and links, while these links may be lost. This study was conducted with the aim of investigating the link and communication between social network users using the centrality criterion and decision tree. Methods: After checking the nodes in the network for each pair of unrelated nodes, some common nodes in the proximity list of these two groups were extracted as common neighbors. Analysis was performed based on common neighbors, association prediction process, and weighted common neighbors. Prediction accuracy improved. Centrality criteria were used to determine the weight of each group. New Big Data techniques were used to calculate centrality measures and store them as features of common neighbors. Personal characteristics of users were added to build complete data for training a data mining model. After modeling, the decision tree model was used to predict communication. Results: There was an increase in sensitivity, which indicated model power in identifying positive categories (i.e., communications) when users' characteristics were used. It means that the model could identify potential latent communications. It can be stated that users are more willing to make a relationship with users similar to them through common neighbors. Personal characteristics of users and centrality were effective in method efficiency, while removal of these properties in the learning process of the decision tree model caused a reduction in efficiency criteria. Conclusion: Prediction of latent communications through social networks was promising. Better results can be obtained from further studies

Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks