In life sciences, the importance of complex network visualization is ever increasing. Yet, existing approaches for the visualization of networks are general purpose techniques that are often not suited to support the specific needs of researchers in the life sciences, or to handle the large network sizes and specific network characteristics that are prevalent in the field. Examples for such net...

Despite entering its fourth year, the rabies outbreak in East Malaysian state of Sarawak has claimed another nine lives 2020, culminating with a total 31 laboratory-confirmed cases human as 31st December 2020. One control challenges faced by authorities within previously rabies-free area, such case Sarawak, is lack information regarding possible starting sources, notably hotspot locations outbr...

COVID-19 causes a jarring impact on the livelihoods of people in Malaysia and globally. To prevent an outbreak community, identifying likely sources infection (hotspots) is important. The goal this study to formulate bipartite network model transmissions by incorporating patient mobility data address assumption population homogeneity made conventional models focus indirect transmission. Two typ...

‎in this paper we give a characterization of unmixed tripartite‎ ‎graphs under certain conditions which is a generalization of a‎ ‎result of villarreal on bipartite graphs‎. ‎for bipartite graphs two‎ ‎different characterizations were given by ravindra and villarreal‎. ‎we show that these two characterizations imply each other‎.

In this paper, we consider the use of the Dulmage-Mendelsohn decomposition and network ow on bipartite graphs to improve a graph bisection partition. Given a graph partition S; B; W ] with a vertex separator S and two disconnected components B and W , diierent strategies are considered based on the Dulmage-Mendelsohn decomposition to reduce the separator size jSj and/or the imbalance between B ...

We apply network analysis to study bipartite consumerservice graph that represents service transaction to understand consumer demand. Based on real-world computer log files of a library, we found that consumer graph projected from bipartite graph deviates significantly from theoretical predictions based on random bipartite graph. We observed smaller-than-expected average degree, larger-than-exp...

Many social networks have a bipartite nature. Link prediction in has been the focus of interest for many researchers recently. Network embedding, which maps each node network to low-dimensional feature vector is used solve problems. The aim this study investigate how embedding enhance link performance networks. A and supervised learning based model presented input learned vectors pairs obtained...

Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, a...