Under most circumstances, the weight information can be obtained by previous studies or research. For the commercial example mentioned above, it can be done by analyzing the past profit reports. Regarding to chemical or biological problems, sometimes if the mechanism of a process is already known and the structures of related receptors are recognized, important features such as active functional groups may be defined so their weights can also be determined. However, this information is frequently unavailable, and researchers are required to mine information from datasets which do not come with any weight information. Sun et al. and Koh et al proposed two graph based methods mining weight information from datasets automatically. In their models, datasets were equivalently transformed and represented by a graph so the concepts and algorithms of the graph can be applied. These two methods did not distinguish the effect of different type of connections on the weight. In the cheminformatics field, the weight information of chemical features comes from connections to "active" compounds and "inactive" compounds. In general, since people are more interested in active features, the contribution from the "active" compounds is preferable. As a result, it is important to discriminate the contributions from different types. Sun et al also presented a weighting framework to address the invalidation of "downward closure property". Other weighted frameworks were also developed to achieve the same goal .