Influential users in online social networks have been given a lot of attention, which could maximize information diffusion in a centralized dissemination process. However, there exist users that can significantly promote diversified information communication among users, which is a pluralistic interaction process. Structural hole (SH) spanners serve as a bridge between social groups, promoting interactions between users and allowing ordinary users to receive diverse information. Filling structural holes, SH spanners have more opportunities to obtain innovative content. Identifying SH spanners is challenging due to their inconspicuous characteristics and privacy policy restrictions. In this article, we are the first to study the SH spanner identification problem using the behavior data generated by users, releasing the dependency on the entire graph structure to find SH spanners. We propose DeepHole to design both the semantic and the sequential behavior analysis modules on users’ generated content, utilizing the TextCNN and Transformer models, respectively. The semantic and sequential embeddings are effective in distinguishing the SH spanners from ordinary users. We conduct comprehensive evaluations using the Yelp and Foursquare datasets simultaneously. Results show that DeepHole can achieve a satisfying performance in detecting SH spanners labeled by both constraint and effective size metrics, with AUC values of 0.923 and 0.933 for Yelp and 0.828 and 0.815 for Foursquare.

Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Different from public schools, the private online education provides diverse courses and satisfy differentiated demands of the students. Students’ behavior and performance in online supplementary learning are relevant to not only personal attributes, but also some factors such as city levels, grades and family situation. Existing studies mostly rely on panel survey/questionnaire data and few studied online private tutoring. In this paper, with 11,392 anonymous K-12 students’ 3-year learning data from one of the world’s largest online extra-curricular education platforms, we uncover students’ online learning behaviors and infer the impact of students’ home location, family socioeconomic situation and attended school’s reputation/rank on the students’ private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students’ performance and behaviors using machine learning algorithms with different groups of features, showing students’ online learning performance can be predicted with MAE< 10%.

The rapid popularity of mobile devices especially smart phones has changed human life style greatly. In this paper, we examine the consumer behaviors on several e-commerce platforms based on a large-scale dataset of mobile internet access records for about 3.5 months from a major telecom operator in China, which covers 126,388 users from Shanghai. We provide a preliminary study on users' daily and periodic online shopping behaviors, as well as the influence of special online shopping events and gender factors. These findings may be exploited by e-commerce providers e.g., for developing personalized recommendation systems to improve their service quality and profit.

In this paper, we study the prediction of co-investment of VCs. We present a series of observation analysis, design a large number of features, and then select prominent features for co-investment by group Lasso. Then we propose a factor graph model SBFG based on structural balance theory to formalize the observation into a unified model. Experiment results show that the proposed method can accurately (around 90% in terms of accuracy) predict the co-investment in the near future with only 10 features selected by group Lasso, and obtains a significant improvement over the baselines.

Venture capital (VC) is of great importance to high-tech industry and network economy since many high-tech firms benefit from VC, especially when they are in their infancy, such as Google, PayPal, and Alibaba. Over 80 percent of the VC investments are related to at least two investors and so co-investment is an important phenomenon in the VC market. However, it is challenging to predict future co-investments due to the complexity and uncertainty of VC behavior. In this paper, we formulate the problem of co-investment prediction into a factor graph model incorporating structural balance theory. We design a large number of features from the perspective of both domain knowledge and social network, and select prominent features by group Lasso. In this paper, we introduce two new investment datasets for the study of VC. Experiment results demonstrate that the proposed model significantly (+9% in terms of accuracy) outperforms the baseline methods. It is shown that only the top 10 features selected by group Lasso (e.g., nationality, number of common neighbors, betweenness, shortest distance, investor type, number of invested fields, and Jaccard similarity of invested fields) can explain the formation of the VC network quite well (around 90 percent in terms of accuracy). In addition, we have some interesting findings. For instance, in the VC network, the co-investor of my co-investor tends to be my co-investor; VC pairs from the same country, of the same investor type, with short distance, with more common neighbors or with appropriate Jaccard similarity of invested fields are likely to co-invest; VCs of large betweenness or of a large number of invested fields have advantage in the VC network; investors of Asian countries, especially of China, are more likely to have social relations than other countries.

We study the problem of group formation in online social networks. In particular, we focus on one of the most important human groups-the triad-and try to understand how closed triads are formed in dynamic networks, by employing data from a large microblogging network as the basis of our study. We formally define the problem of triadic closure prediction and conduct a systematic investigation. The study reveals how user demographics, network characteristics, and social properties influence the formation of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in a dynamic network. Different kernel functions are incorporated into the proposed graphical model to quantify the similarity between triads. Our experimental results with the large microblogging dataset demonstrate the effectiveness (+10 percent over alternative methods in terms of F1-Score) of the proposed model for the prediction of triadic closure formation.

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy.