A methodology framework for bipartite network modeling

The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the dynamic interaction and relationship among the vertices. Nonetheless, incorporating the features of individual vertex and capturing the dynamic interaction of the heterogeneous local rules governing each of them in the studies is lacking. The methodology in achieving this could hardly be found. Consequently, this study intends to propose a methodology framework that considers the influence of heterogeneous features of each node to the overall network behavior in modeling real-world bipartite network system. The proposed framework consists of three main stages with principal processes detailed in each stage, and three libraries of techniques to guide the modeling activities. It is iterative and process-oriented in nature and allows future network expansion. Two case studies from the domain of communicable disease in epidemiology and habitat suitability in ecology employing this framework are also presented. The results obtained suggest that the methodology could serve as a generic framework in advancing the current state of the art of bipartite network approach

Web Algorithm search engine based network modelling of Malaria Transmission

Malaria has been described as one of the most dangerous and widest spread tropical diseases, with an estimated 247 million cases around the globe in the year 2006 alone. This calls for urgent scientific interventions. Since malaria is a vector borne disease, this research tackled the issue of malaria transmission from the angle of vector detection through a search engine. There are observed cases of attempting vector control on a trial and errors basis, with no scientific way of determining the locations of critical vector densities. Unfortunately, such a practice leads to waste of resources on the wrong places, while ignoring the areas of critical vector existence. This research formalizes a contact network using a number of attributes of the malaria vectors, the public places, and the human beings that affect malaria transmission. The resulting structure is a heterogeneous bipartite contact network of two node types - the public places and the human beings nodes. The human beings are those who have suffered from malaria, even when their residential homes were under reliable vector control. Such an exclusion principle makes it obvious that these people, most probably contacted the disease from outside their residential homes. The Hypertext Induced Topical Search (HITS) web search algorithm was adapted to implement a search engine, which uses the bipartite contact network as the input. MATLAB was used to implement the model system. The output shows the public places which habour the infected malaria vectors, and their corresponding vector densities. The model output was validated with UCINET 6.0 as the benchmark system. A root mean square error (RMSE) value of 0.0023 was obtained when the output of the benchmark system is compared with that of the search engine model. This result indicates a high and acceptable level of accuracy

A methodology framework for bipartite network modeling

Abstract The graph-theoretic based studies employing bipartite network approach mostly focus on surveying the statistical properties of the structure and behavior of the network systems under the domain of complex network analysis. They aim to provide the big-picture-view insights of a networked system by looking into the dynamic interaction and relationship among the vertices. Nonetheless, incorporating the features of individual vertex and capturing the dynamic interaction of the heterogeneous local rules governing each of them in the studies is lacking. The methodology in achieving this could hardly be found. Consequently, this study intends to propose a methodology framework that considers the influence of heterogeneous features of each node to the overall network behavior in modeling real-world bipartite network system. The proposed framework consists of three main stages with principal processes detailed in each stage, and three libraries of techniques to guide the modeling activities. It is iterative and process-oriented in nature and allows future network expansion. Two case studies from the domain of communicable disease in epidemiology and habitat suitability in ecology employing this framework are also presented. The results obtained suggest that the methodology could serve as a generic framework in advancing the current state of the art of bipartite network approach. Graphical Abstrac