Minimizing the Spread of Negative Influence in SNIR Model by Contact Blocking

This paper presents a method to minimize the spread of negative influence on social networks by contact blocking. First, based on the infection-spreading process of COVID-19, the traditional susceptible, infectious, and recovered (SIR) propagation model is extended to the susceptible, non-symptomatic, infectious, and recovered (SNIR) model. Based on this model, we present a method to estimate the number of individuals infected by a virus at any given time. By calculating the reduction in the number of infected individuals after blocking contacts, the method selects the set of contacts to be blocked that can maximally reduce the affected range. The selection of contacts to be blocked is repeated until the number of isolated contacts that need to be blocked is reached or all infection sources are blocked. The experimental results on three real datasets and three synthetic datasets show that the algorithm obtains contact blockings that can achieve a larger reduction in the range of infection than other similar algorithms. This shows that the presented SNIR propagation model can more precisely reflect the diffusion and infection process of viruses in social networks, and can efficiently block virus infections

A Parallel & Distributed Method for Computing High Dimensional MOLAP *

Abstract. Data cube has been playing an essential role in fast OLAP(on-line analytical processing) in many multidimensional data warehouse. We often execute range queries on aggregate cube computed by pre-aggregate technique in MOLAP. For the cube with d dimensions, it can generate 2 d cuboids. But in a high-dimensional data warehouse (such as the applications of bioinformatics and statistical analysis, etc.), we build all these cuboids and their indices and full materialized the data cube impossibly. In this paper, we propose a multidimensional hierarchical fragmentation of the fact table based on dimension hierarchical encoding. This method partition the high dimensional data cube into shell mini-cubes. Using dimension hierarchical encoding and pre-aggregated results, OLAP queries are computed online by dynamically constructing cuboids from the fragment data cubes. Such an approach permits a significant reduction of processing and I/O overhead for many queries by restricting the number of fragments to be processed for both the fact table and bitmap encoding data. This method also supports parallel I/O and parallel processing as well as load balancing for disks and processors. We have compared the methods of our parallel method with the other existed ones such as partial cube by experiment. The analytical and experimental results show that the method of our parallel method proposed in this paper is more efficient than the other existed ones