Link Prediction with Personalized Social Influence

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Link prediction is of great interest recently since one of the most important goals of social networks is to connect people, so that they can interact with their friends from real world or make new friend through Internet. So the predicted links in social networks can be helpful for people to have connections with each others. Other than the pure topological network structures, social networks also have rich information of social activities of each user, such as tweeting, retweeting, and replying activities. Social science theories, such as social influence, suggests that the social activities could have potential impacts on the neighbors, and links in social networks are the results of the impacts taking place between different users. It motivates us to perform link prediction by taking advantage of the activity information. There has been a lot of proposed methods to measure the social influence through user activity information. However, traditional methods assigned some social influence measures to users universally based on their social activities, such as number of retweets or mentions the users have. But the social influence of one user towards others may not always remain the same with respect to different neighbors, which demands a personalized learning schema. Moreover, learning social influence from heterogeneous social activities is a nontrivial problem, since the information carried in the social activities is implicit and sometimes even noisy. Motivated by time-series analysis, we investigate the potential of modeling influence patterns based on pure timestamps, i.e., we aim to simplify the problem of processing heterogeneous social activities to a sequence of timestamps. Then we use timestamps as an abstraction of each activity to calculate the reduction of uncertainty of one users social activities given the knowledge of another one. The key idea is that, if a user i has impact on another user j, then given the activity timestamps of user i, the uncertainty in user j’s activity timestamps could be reduced. The uncertainty is measured by entropy in information theory, which is proven useful to detect the significant influence flow in time-series signals in information-theoretic applications. By employing the proposed influence metric, we incorporate the social activity information into the network structure, and learn a unified low-dimensional representation for all users. Thus, we could perform link prediction effectively based on the learned representation. Through comprehensive experiments, we demonstrate that the proposed method can perform better than the state-of-the-art methods in different real-world link prediction tasks

Link Prediction with Personalized Social Influence

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Link prediction is of great interest recently since one of the most important goals of social networks is to connect people, so that they can interact with their friends from real world or make new friend through Internet. So the predicted links in social networks can be helpful for people to have connections with each others. Other than the pure topological network structures, social networks also have rich information of social activities of each user, such as tweeting, retweeting, and replying activities. Social science theories, such as social influence, suggests that the social activities could have potential impacts on the neighbors, and links in social networks are the results of the impacts taking place between different users. It motivates us to perform link prediction by taking advantage of the activity information. There has been a lot of proposed methods to measure the social influence through user activity information. However, traditional methods assigned some social influence measures to users universally based on their social activities, such as number of retweets or mentions the users have. But the social influence of one user towards others may not always remain the same with respect to different neighbors, which demands a personalized learning schema. Moreover, learning social influence from heterogeneous social activities is a nontrivial problem, since the information carried in the social activities is implicit and sometimes even noisy. Motivated by time-series analysis, we investigate the potential of modeling influence patterns based on pure timestamps, i.e., we aim to simplify the problem of processing heterogeneous social activities to a sequence of timestamps. Then we use timestamps as an abstraction of each activity to calculate the reduction of uncertainty of one users social activities given the knowledge of another one. The key idea is that, if a user i has impact on another user j, then given the activity timestamps of user i, the uncertainty in user j’s activity timestamps could be reduced. The uncertainty is measured by entropy in information theory, which is proven useful to detect the significant influence flow in time-series signals in information-theoretic applications. By employing the proposed influence metric, we incorporate the social activity information into the network structure, and learn a unified low-dimensional representation for all users. Thus, we could perform link prediction effectively based on the learned representation. Through comprehensive experiments, we demonstrate that the proposed method can perform better than the state-of-the-art methods in different real-world link prediction tasks

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

Understanding and Predicting Delay in Reciprocal Relations

Reciprocity in directed networks points to user's willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.Comment: 10 page