Cyber-physical-social big data generated from ubiquitous devices and diverse spaces generally are multi-source, heterogeneous, and deeply intertwined. To efficiently analyze and handle the ubiquitous cyber-physical-social big data, tensor is considered as an effective tool, but the curse of dimensionality is still the main bottleneck of tensor-based big data analysis. Tensor networks can considerably alleviate or overcome it through the tensor approximate theory. Therefore, this paper focuses on developing an efficient big data processing framework based on tensor networks and providing an incremental tensor train decomposition approach for the streaming big data. Concretely, this paper first presents a hierarchical cyber-physical-social big data processing framework composed of three planes, namely, data representation and decomposition, data storage and processing, and data analysis and service. Then, tensor train (TT) and quantized TT decompositions are particularly introduced to remarkably overcome the curse of dimensionality. Furthermore, to efficiently handle the continuous streaming big data and avoid the repeated decomposition for the history data, an incremental tensor train decomposition (ITTD) approach is proposed and the complexities are further analyzed in detail. Experimental results demonstrate that ITTD demonstrably outperforms the nonincremental TT decomposition in execution time on the precise of guaranteeing the nearly equal approximation error.

The core number of vertices is a basic index depicting cohesiveness of a graph, and has been widely used in large-scale graph analytics. In this paper, we study the update of core numbers of vertices in dynamic graphs with edge insertions/deletions, which is known as the core maintenance problem. Different from previous approaches that just focus on the case of single-edge insertion/deletion and sequentially handle the edges when multiple edges are inserted/deleted, we investigate the parallelism in the core maintenance procedure. Specifically, we show that if the inserted/deleted edges constitute a matching, the core number update with respect to each inserted/deleted edge can be handled in parallel. Based on this key observation, we propose parallel algorithms for core maintenance in both cases of edge insertions and deletions. Extensive experiments are conducted to evaluate the efficiency, stability, parallelism and scalability of our algorithms on different types of real-world, synthetic graphs and temporal networks. Comparing with former approaches, our algorithms can improve the core maintenance efficiency significantly.

Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the cyber-physical-social systems (CPSS). In this context, computational experiments method has emerged as a novel approach for the design, analysis, management, control, and integration of CPSS, which can realize the causal analysis of complex systems by means of “algorithmization” of “counterfactuals”. However, because CPSS involve human and social factors (e.g., autonomy, initiative, and sociality), it is difficult for traditional design of experiment (DOE) methods to achieve the generative explanation of system emergence. To address this challenge, this paper proposes an integrated approach to the design of computational experiments, incorporating three key modules: 1) Descriptive module: Determining the influencing factors and response variables of the system by means of the modeling of an artificial society; 2) Interpretative module: Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena; 3) Predictive module: Building a meta-model that is equivalent to artificial society to explore its operating laws. Finally, a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach, which can reveal the social impact of algorithmic behavior on “rider race”.

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multitask multiview learning in this article. We first explain the feasibility and advantages of multitask multiview learning for these two tasks. Then we propose a novel model named MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multiview graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view the attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network data sets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks.

With the development of GPS technology, GPS-equipped taxicabs and users with GPS-enabled devices interact each other, and form a new mobile Internet of Things (M-IoT). In this paper, we propose a M-IoT service framework to predict multi-users' mobility pattern by solving a cubical User-Spatio-Temporal profitability map arising from heterogeneous sensor data. Then the framework provides the service for the traffic participant based on the prediction. To solve the stationary probability map, a novel tensor-based iterative algorithm is proposed and proved to be convergent, at the same time, we show the existence and uniqueness of the stationaryprobability. This paper's highlight is the multivariate multi-step transition tensor ( $M^2 T^2$ ) model that leverages tensor to merge massivesensor data from multi-source, including time, space, and social network so on, in a unified expression and construct the multivariate transition tensor. In particular, as the theoretical basis of the $M^2 T^2$ model, a novel concept named eigentensor is proposed in this paper. The context-aware $M^2 T^2$ model takes into account comprehensive context factors to improve the accuracy of prediction.The results show that our model has much better prediction accuracy than other context-less models.