People go to fortune tellers in hopes of learning things about their future. A future career path is one of the topics most frequently discussed. But rather than rely on "black arts" to make predictions, in this work we scientifically and systematically study the feasibility of career path prediction from social network data. In particular, we seamlessly fuse information from multiple social networks to comprehensively describe a user and characterize progressive properties of his or her career path. This is accomplished via a multi-source learning framework with fused lasso penalty, which jointly regularizes the source and career-stage relatedness. Extensive experiments on real-world data confirm the accuracy of our model.

Detection of overlapping communities has drawn much attention lately as they are essential properties of real complex networks. Despite its influence and popularity, the well studied and widely adopted stochastic model has not been made effective for finding overlapping communities. Here we extend the stochastic model method to detection of overlapping communities with the virtue of autonomous determination of the number of communities. Our approach hinges upon the idea of ranking node popularities within communities and using a Bayesian method to shrink communities to optimize an objective function based on the stochastic generative model. We evaluated the novel approach, showing its superior performance over five state-of-the-art methods, on large real networks and synthetic networks with ground-truths of overlapping communities.

Social networks contain a wealth of useful information. In this paper, we study a challenging task for integrating users' information from multiple heterogeneous social networks to gain a comprehensive understanding of users' interests and behaviors. Although much effort has been dedicated to study this problem, most existing approaches adopt linear or shallow models to fuse information from multiple sources. Such approaches cannot properly capture the complex nature of and relationships among different social networks. Adopting deep learning approaches to learning a joint representation can better capture the complexity, but this neglects measuring the level of confidence in each source and the consistency among different sources. In this paper, we present a framework for multiple social network learning, whose core is a novel model that fuses social networks using deep learning with source confidence and consistency regularization. To evaluate the model, we apply it to predict individuals' tendency to volunteerism. With extensive experimental evaluations, we demonstrate the effectiveness of our model, which outperforms several state-of-the-art approaches in terms of precision, recall and F1-score.

Neural machine translation (NMT) conducts end-to-end translation with a source language encoder and a target language decoder, making promising translation performance. However, as a newly emerged approach, the method has some limitations. An NMT system usually has to apply a vocabulary of certain size to avoid the time-consuming training and decoding, thus it causes a serious out-of-vocabulary problem. Furthermore, the decoder lacks a mechanism to guarantee all the source words to be translated and usually favors short translations, resulting in fluent but inadequate translations. In order to solve the above problems, we incorporate statistical machine translation (SMT) features, such as a translation model and an n-gram language model, with the NMT model under the log-linear framework. Our experiments show that the proposed method significantly improves the translation quality of the state-ofthe-art NMT system on Chinese-to-English translation tasks. Our method produces a gain of up to 2.33 BLEU score on NIST open test sets.

In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task in location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenario, human exhibits distinct mobility patterns, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods.

Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and knowledge from reliable (usually highly-reputable) answerers has become an increasingly popular research topic. A major challenge in cQA tasks is the accurate matching of high-quality answers w.r.t given questions. Many of traditional approaches likely recommend corresponding answers merely depending on the content similarity between questions and answers, therefore suffer from the sparsity bottleneck of cQA data. In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. More specifically, our framework collaboratively utilizes the rich interaction among questions, answers and answerers to learn the relative quality rank of different answers w.r.t a same question. Moreover, the information in heterogeneous social networks is comprehensively employed to enhance the quality of question-answering (QA) matching by our deep random walk learning framework. Extensive experiments on a large-scale dataset from a real world cQA site show that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cQA methods.

Social media platforms have become the most popular means for users to share what is happening around them. The abundance and growing usage of social media has resulted in a large repository of users' social posts, which provides a stethoscope for inferring individuals' lifestyle and wellness. As users' social accounts implicitly reflect their habits, preferences, and feelings, it is feasible for us to monitor and understand the wellness of users by harvesting social media data towards a healthier lifestyle. As a first step towards accomplishing this goal, we propose to automatically extract wellness events from users' published social contents. Existing approaches for event extraction are not applicable to personal wellness events due to its domain nature characterized by plenty of noise and variety in data, insufficient samples, and inter-relation among events.To tackle these problems, we propose an optimization learning framework that utilizes the content information of microblogging messages as well as the relations between event categories. By imposing a sparse constraint on the learning model, we also tackle the problems arising from noise and variation in microblogging texts. Experimental results on a real-world dataset from Twitter have demonstrated the superior performance of our framework.

Psychological theories suggest that emotion represents the state of mind and instinctive responses of one’s cognitive system (Cannon 1927). Emotions are a complex state of feeling that results in physical and psychological changes that influence our behavior. In this paper, we study an interesting problem of emotion contagion in social networks. In particular, by employing an image social network (Flickr) as the basis of our study, we try to unveil how users’ emotional statuses influence each other and how users’ positions in the social network affect their influential strength on emotion. We develop a probabilistic framework to formalize the problem into a role-aware contagion model. The model is able to predict users’ emotional statuses based on their historical emotional statuses and social structures. Experiments on a large Flickr dataset show that the proposed model significantly outperforms (+31% in terms of F1-score) several alternative methods in predicting users’ emotional status. We also discover several intriguing phenomena. For example, the probability that a user feels happy is roughly linear to the number of friends who are also happy; but taking a closer look, the happiness probability is superlinear to the number of happy friends who act as opinion leaders (Page et al. 1999) in the network and sublinear in the number of happy friends who span structural holes (Burt 2001). This offers a new opportunity to understand the underlying mechanism of emotional contagion in online social networks.

People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fully-overlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially overlapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTrans. To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user's behaviors in different platforms. Extensive experiments across two real social networks show that XPTrans significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPTrans can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms.

An ultra-realistic robot was unveiled last week by researchers from the University of Science and Technology in China (USTC). Jia Jia, as the female robot has been named, is apparently capable of basic communication, interaction with nearby people, and natural facial expressions. Unfortunately, many of her pre-programmed interactions appear to be highly stereotypical.