In online social networks, information diffusion presents a complicated dynamic process that accompanies users' lives, in which the link between multi-information symbiosis and conflict is frequently overlooked. We investigate two aspects that influence the process of diffusion by examining the phenomena of information dissemination between individuals and the general environment in online social networks. To characterize that complicated process, an effective model of positive and negative information diffusion based on evolutionary game theory is developed. To reduce the impact of negative information on the network ecosystem as much as possible while increasing the propagation of positive information, real-time control mechanisms are provided, and a corresponding optimal problem of control resource allocation is constructed. Considering some special circumstances that cannot monitor the diffusion process in real time, the pulse control strategies are offered. The efficiency of control systems is validated by theoretical evidence and experiments, and the impact of various factors on information dispersion is examined. The work will contribute to network management by providing a theoretical framework for understanding and managing the distribution of diverse types of information.

Cross-project defect prediction (CPDP) refers to identifying defect-prone software modules in one project (target) using historical data collected from other projects (source), which can help developers find bugs and prioritize their testing efforts. Recently, CPDP has attracted great research interest. However, the source and target data usually exist redundancy and nonlinearity characteristics. Besides, most CPDP methods do not exploit source label information to uncover the underlying knowledge for label propagation. These factors usually lead to unsatisfactory CPDP performance. To address the above limitations, we propose a landmark selection-based kernelized discriminant subspace alignment (LSKDSA) approach for CPDP. LSKDSA not only reduces the discrepancy of the data distributions between the source and target projects, but also characterizes the complex data structures and increases the probability of linear separability of the data. Moreover, LSKDSA encodes label information of the source data into domain adaptation learning process and makes itself with good discriminant ability. Extensive experiments on 13 public projects from three benchmark datasets demonstrate that LSKDSA performs better than a range of competing CPDP methods. The improvement is 3.44%-11.23% in g-measure, 5.75%-11.76% in AUC, and 9.34%-33.63% in MCC, respectively.

E-commerce business process nets (EBPNs) are a novel formal model for describing and validating e-commerce systems including interactive parties such as shopper, merchant, and third-party payment platform. Data errors and nondeterminacy of the data states during the trading process can be depicted with the help of EBPNs. However, the problem about how to analyze EBPNs remains largely open. To analyze their data-liveness, data-boundedness, and reachability, this paper presents two analysis methods. For EBPNs, reachability analysis is proposed based on a 3-D incidence matrix method. Additionally, reduction methods are proposed for a special EBPN. Finally, the validity and reliability of the proposed methods are illustrated via the examples of e-commerce systems.

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Conceptual modeling, which includes both control-flow and data-flow modeling, has posed great challenges for conventional business process management systems. To support systematic data-flow modeling and analysis, in this paper, we propose a novel and effective Petri net-based approach. We first introduce a new type of Petri net, called WFIO-net, the firing rule of which is formally defined by extending classical Petri net with read and write semantics, to model both the control-flow and data-flow information. Then, we discuss about three possible data-flow errors over a WFIO-net, i.e., the missing, redundant and conflicting data errors. To detect such data-flow errors, we develop a polynomial complexity algorithm based on the so-called Activity Data Incidence Matrix (ADIM) of a WFIO-net. Following by that, we propose three effective correction strategies to resolve the detected data-flow anomalies. Finally, we present a property loan approval business process case study for our approach, and the study results demonstrate that the proposed detection and correction approaches are indeed very effective and can be applied to real-life settings.