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.

Reliability is a key challenge to be understood to turn the vision of exascale supercomputing into reality. Inevitably, large-scale supercomputing systems, especially those at the peta/exascale levels, must tolerate failures, by incorporating fault-tolerance mechanisms to improve their reliability and availability. As the benefits of fault-tolerance mechanisms rarely come without associated time and/or capital costs, reliability will limit the scalability of parallel applications. This paper introduces for the first time the concept of "Reliability Wall” to highlight the significance of achieving scalable performance in peta/exascale supercomputing with fault tolerance. We quantify the effects of reliability on scalability, by proposing a reliability speedup, defining quantitatively the reliability wall, giving an existence theorem for the reliability wall, and categorizing a given system according to the time overhead incurred by fault tolerance. We also generalize these results into a general reliability speedup/wall framework by considering not only speedup but also costup. We analyze and extrapolate the existence of the reliability wall using two representative supercomputers, Intrepid and ASCI White, both employing checkpointing for fault tolerance, and have also studied the general reliability wall using Intrepid. These case studies provide insights on how to mitigate reliability-wall effects in system design and through hardware/software optimizations in peta/exascale supercomputing.