Predictive network analytics for national research investment

Abstract

Research is a risky business. The starting point of research is ignorance: if we already have answers to our questions or simply undertaking routine works to get answers, we wouldn't be undertaking research in the first instance. Australia spends approximately 2.2% GDP (or $27.7B AUD) in research and development. So we are taking considerable risks as a country. Fortunately, some research areas are less risky than others. They have well-established theoretical foundations and experimental methodologies, proper access to infrastructures and equipment, and above all a critical mass of researchers to advance the state of knowledge. In "emerging" areas of research however the risks are considerably higher there may not be an established theory, methodology, or even a critical mass of researchers available. Finding the right approach to fund emerging research is a serious policy challenge. The European Research Council and the National Science Foundation (in the U.S.) have both independently initiated works in developing approaches to identify and fund emerging research in recent years. If we accept the suggestion that research investment is akin to portfolio investment (to maximize the expected return while minimize risk over an entire investment portfolio), then investing in emerging research amounts to investing in high risk options with high expected return. But how do we pick "winners" from "imposers"? How can we tell we are not picking "one hit wonders"? How can we spot "sleeping beauties" which may take years to mature? The availability of large scale global bibliographic (and other relevant) data from both open and commercial sources presents an intriguing opportunity for data miners and machine learners to contribute to these debates. In this presentation, we shall examine the general shape of the problem definition to look at the "why" and "what" instead of the "how". Our aim is to present and engage the Australasian data mining and machine learning communities in a conversation about an intellectually challenging and exciting problem that can have wide spread impact on how governments, funding agencies and industries make strategic decisions in R&D investment. Show more