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

Modeling user navigation

This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed

The Lifecycles of Apps in a Social Ecosystem

Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways --- they exhibit a rich temporal structure of user adoption and long-term engagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been difficult, however, to study apps in their natural setting since this requires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit. In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties. At the temporal level, we develop a retention model that represents a user's tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two fundamental axes --- popularity and sociality --- and we show how a user's probability of adopting an app depends both on properties of the local network structure and on the match between the user's attributes, his or her friends' attributes, and the dominant attributes within the app's user population. We also develop models that show the importance of different feature sets with strong performance in predicting app success.Comment: 11 pages, 10 figures, 3 tables, International World Wide Web Conferenc

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur