The computational social science could be a method that suitable for us to analyze data deeply. The capacity to collect and analyze massive amounts of data has transformed such fields as biology and physics. The data could be like data sets of millions of people, including location, financial transactions and communications. Communications means how people interact surely offer quantitatively of new perspectives on collective human behavior.
Currently, existing data sets are scattered among many groups, with uneven skills. it is good for us to know easily the data sets for research for example. The computational social science is including to sustainability science because i think the knowledge will stands long on our life.
The “New” Network Science
It proposed a model of generalized affiliation networks in which distance between groups is defined according to some number of social dimensions(e.g.,geography and occupation) , and individuals are characterized by the coordinates of the groups to which they belong. Ties between individuals are then allowed to form with a probability that depends on the distance between the corresponding groups and a tunable homophily (Lazarsfeld & Merton 1954) parameter that biases interactions toward or away from similar nodes. Scale-Free Networks consist of degree distribution which is typically right-skewed with the majority of nodes have less than average degree and a small fraction hubs are many times better connected than average. a practical problem as a combination of the empirical between small-world and scale-free networks that the increasing extent of online activity (as a means of communicating, conducting business, recording activities, etc.) may help at least partially overcome. This is could be effective activities. Network Motifs is like the network that symbolize communities of knowledge for example the World Wide Web. Community Structure is an intermediate scale of analysis between local for example clustering, network motifs and global like connectivity, path lengths structure. Based on the empirical data, local, global, or community are an important but typically overlooked distinction is that between what might be called “symbolic” networks, which can be thought of as network representations of abstract relations between discrete entities, and “interactive” networks, whose links describe tangible interactions that are capable of transmitting information, influence, or material. the relationship between network structure and dynamical consequences is anything but straight forward. For example, itisal most certainly the case that the detail so network structure (as in statistical measures like triad densities or degree distributions) that are relevant to individual and collective behavior will depend on the nature of the particular dynamical process under investigation. Ultrarobust networks that the networks simultaneously minimize the likelihood of individual node failure (due to endogenously generated congestion), and also the impact on global connectivity that results from failures arising out of either endogenous or exogenous causes. The key to ultrarobustness, they find, is that organizational networks must exhibit nonhierarchical ties that extend across all scales.
Fauzi Ahmad (1401164633) – MB-40-INT-2