DyNet implements social network theory to elucidate at a glance interconnections and relationships among selected data and their associated strengths, significance, embeddedness plus a strong feature such as dynamical changes through time to unveil trends and positioning of competitors. DyNet can be used, in science knowledge bases, to check for therapeutic areas or drug targets and their evolution in time in terms of publications, key scientists involved and their relationships, competitors, patents (forward citations), chemicals, NCEs and many more. DyNet (figures ) will display data in terms of NODES (subject of your data: people, companies, shares, products, publications, patents) with LINKS to other nodes to which a connection has been established in the data set. Links thickness will generally determine the strength of that relationships whilst node size generally reflects the importance of that node within the network. Complex networks can be filtered according to attributes or other values loaded with the dataset together with colour editing in order to group or highlight relevant nodes.

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