It is critical and important for venture investors to find high-potential startups at their early stages. Indeed, many efforts have been made to study the key factors for the success of startups through the topological analysis of the heterogeneous information network of people, startup, and venture firms or representation learning of latent startup profile features. However, the existing topological analysis lacks an in-depth understanding of heterogeneous information. Also, the approach based on representation learning heavily relies on domain-specific knowledge for feature selections. Instead, in this paper, we propose aScalable Heterogeneous Graph Markov Neural Network (SHGMNN) for identifying the high-potential startups. The general idea is to use graph neural networks (GNN) to learn effective startup representations through end-to-end efficient training and model the label dependency among startups through Maximum A Posterior (MAP) inference. Specifically, we first define different metapaths to capture various semantics over the heterogeneous information network (HIN) and aggregate all semantic information into a summated graph structure. To predict the high-potential early-stage startups, we introduce GNN to diffuse the information over the summated graph. We then adopt an MAP inference over Hinge-Loss Markov Random Fields to enforce label dependency. Here, a pseudolikelihood variational expectation-maximization (EM) framework is incorporated to optimize both MAP inference and GNN iteratively: The E-step calculates the inference, and the M-step updates the GNN. For efficiency concerns, we develop a GNN with a lightweight linear diffusion architecture to perform graph propagation over web-scale heterogeneous information networks. Finally, extensive experiments and case studies on real-world datasets demonstrate the superiority of SHGMNN.

With recent advances in mobile and sensor technologies, a large amount of efforts have been made on developing intelligent applications for taxi drivers, which provide beneficial guidance for improving the profit and work efficiency. However, limited scopes focus on the latent social interactions within cab drivers, and corresponding social learning mechanism to share driving behavior patterns has been largely ignored. To that end, in this paper, we propose a comprehensive study to discover how social learning affects taxi drivers' driving behaviors. To be specific, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively measuring the latent propagation of driving patterns within taxi drivers. Validations on a real-word data set collected from New York City clearly verify the effectiveness of our proposed framework with better explanation of future taxi driving pattern evolution, which prove the hypothesis that social factors indeed improve the predictability of taxi driving behaviors, and further reveal some interesting rules on social learning mechanism.

It is commonly seen that social events are organized through online social network services (SNSs), and thus there are vested interests in studying event-oriented social gathering through SNSs. The focus of existing studies has been put on the analysis of event profiles or individual participation records. While there is significant dynamic mutual influence among target users through their social connections, the impact of dynamic mutual influence on the people's social gathering remains unknown. To that end, in this paper, we develop a discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a two-stage variant discriminant framework to capture the users' profiles as well as their latent social connections. The validation on real-world data sets show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines. This validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation. Moreover, we propose the network pruning method to further improve the efficiency of our technical framework. Finally, we provide a case study to illustrate the application of our framework for event plan design task.

Recent years have witnessed the boom of venture capital industry. Venture capitalists can attain great financial rewards if their invested companies exit successfully, via being acquired or going IPO (Initial Public Offering). The literature has revealed that, from both financial and managerial perspectives, decision-making process and successful rates of venture capital (VC) investments can be greatly improved if the investors well know the team members of target startups. However, much less efforts have been made on understanding the impact of prominent social ties between the members of VC firms and start-up companies on investment decisions. To this end, we propose to study such social relationship and see how this information can contribute to foreseeing investment deals. We aim at providing analytical guidance for the venture capitalists in choosing right investment targets. Specifically, we develop a Social-Adjusted Probabilistic Matrix Factorization (PMF) model to exploit members social connections information from VC firms and startups for investment recommendations. Unlike previous studies, we make use of the directed relationship between any pair of connected members from the two institutions respectively and quantify the variety of social network groups. As a result, it brings in much more flexibility, and the modeling results inherently provide meaningful managerial implications for the operators of VC firms and startups. Finally, we evaluate our model on both synthetic and real-world data. The results demonstrate that our approach outperforms the baseline algorithms with a significant margin.

With recent advances in mobile and sensor technologies, a large amount of efforts have been made on developing intelligent applications for taxi drivers, which provide beneficial guide and opportunity to improve the profit and work efficiency. However, limited scopes focus on the latent social interaction within cab drivers, and corresponding social propagation scheme to share driving behaviors has been largely ignored. To that end, in this paper, we propose a comprehensive study to reveal how the social propagation affects for better prediction of cab drivers' future behaviors. To be specific, we first investigate the correlation between drivers' skills and their mutual interactions in the latent vehicle-to-vehicle network, which intuitively indicates the effects of social influences. Along this line, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively revealing the latent driving pattern propagation within taxi drivers. Comprehensive experiments on a real-word data set collected from the New York City clearly validate the effectiveness of our proposed framework on predicting future taxi driving behaviors, which also support the hypothesis that social factors indeed improve the predictability of driving behaviors.

Bike sharing systems, aiming at providing the missing links in the public transportation systems, are becoming popular in urban cities. In an ideal bike sharing network, the station locations are usually selected in a way that there are balanced pick-ups and drop-offs among stations. This can help avoid expensive re-balancing operations and maintain high user satisfaction. However, it is a challenging task to develop such an efficient bike sharing system with appropriate station locations. Indeed, the bike station demand is influenced by multiple factors of surrounding environment and complex public transportation networks. Limited efforts have been made to develop demand-and-balance prediction models for bike sharing systems by considering all these factors. To this end, in this paper, we propose a bike sharing network optimization approach by considering multiple influential factors. The goal is to enhance the quality and efficiency of the bike sharing service by selecting the right station locations. Along this line, we first extract fine-grained discriminative features from human mobility data, point of interests (POI), as well as station network structures. Then, prediction models based on Artificial Neural Networks (ANN) are developed for predicting station demand and balance. In addition, based on the learned patterns of station demand and balance, a genetic algorithm based optimization model is built to choose a set of stations from a large number of candidates in a way such that the station usage is maximized and the number of unbalanced stations is minimized. Finally, the extensive experimental results on the NYC CitiBike sharing system show the advantages of our approach for optimizing the station site allocation in terms of the bike usage as well as the required re-balancing efforts.

Nowadays, it is commonly seen that an offline social event is organized through online social network services (SNS), in this way cyber strangers can be connected in physical world. While there are some preliminary studies on social event participation through SNS, they usually have more focus on the mining of event profiles and have less focus on the social relationships among target users. In particular, the importance of dynamic mutual influence among potential event participants has been largely ignored. In this paper, we develop a novel discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a variant two-stage discriminant framework to capture the users' preferences as well as their latent social connections. The experimental results on real-world data show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines, which validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation.