Knowledge Discovery from Networks

Nowadays, network becomes the engine of scientific research activities in 21st century. For example, a Web search engine is something to do with networked data mining and knowledge discovery from networks in deed. Networks interact with one another and are recursive. We have come to grasp the important knowledge of networks. Network is the key to representing the complex world around us. Small changes in the topology, affecting only a few of the nodes, can open up hidden doors, allowing new possibilities to emerge. While network mining is considered in my talk, it is always stressed and focused on a kernel idea, i.e. topology first, mainly concerning the self-organization, self-similarity and emergency features. Taking network topology as a novel approach of knowledge representation, we discuss how to mine typical topology patterns from real world networks at multi-scale, to evaluate node importance for node-ranking, to evaluate edge importance for edge-ranking, and to discover the membership for different communities in a network as well. Brain science has achieved a great success on molecule-level and cell-level research; however, there is still a long way to go for cognitive function of a brain as a whole. How can we understand the non-linear function of a brain? How does the left brain (with the priority of logic thinking) cooperate with the right brain (with the priority of visual thinking)? How far away for "von-Neumann-style" computer architecture? May the future computer architecture consist of dual core, one for logic thinking and the other for visual thinking, which correlate each other all the time? May the future operating systems are developed under the mechanism of "growth by preferential attachment"? I am interested in all these questions in mytalk.