{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/FT250100159"},"data":{"type":"grant-details","id":"FT250100159","attributes":{"code":"FT250100159","administering-organisation":"Macquarie University","announcement-administering-organisation":"Macquarie University","scheme-name":"ARC Future Fellowships","grant-status":"Active","funding-commencement-year":2025,"years-funded":4,"project-start-date":"2026-06-30","anticipated-end-date":"2030-06-29","grant-summary":"Next-Generation Graph-Level Mining for High-Complexity Data Environments. This project aims to advance Australia's data mining research by developing cutting-edge graph-level mining solutions for highly complex real-world data environments, such as multi-source, low-quality, and multi-graph data. The research will primarily explore intricate structural interactions in challenging scenarios to generate new knowledge and enable effective, reliable, and robust graph analysis with interpretability. This project will lay the theoretical foundations of this significant research field to strengthen Australia’s world leadership role in data science. Practically, the outcomes should benefit Australian governments and businesses across diverse applications, such as intelligent urban transportation and financial networks.","funding-current":1197863.00,"funding-at-announcement":1173569,"investigators-current":[{"title":"Prof","firstName":"Jia","familyName":"Wu","roleName":"Future Fellowship","roleCode":"FT","isFellowship":true,"orcidIdentifier":"0000-0002-1371-5801 "}],"investigators-at-announcement":[{"title":"Prof","firstName":"Jia","familyName":"Wu","roleName":"Future Fellowship","roleCode":"FT","isFellowship":true,"orcidIdentifier":"0000-0002-1371-5801 "}],"organisations-current":[{"organisationName":"Macquarie University","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"Macquarie University","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":true,"code":"4605","name":"Data Management and Data Science","type":"FOR20"},{"isPrimary":false,"code":"460502","name":"Data Mining and Knowledge Discovery","type":"FOR20"},{"isPrimary":false,"code":"460505","name":"Database Systems","type":"FOR20"},{"isPrimary":false,"code":"460506","name":"Graph, Social and Multimedia Data","type":"FOR20"}],"socio-economic-objective":[{"code":"280115","name":"Expanding Knowledge In the Information and Computing Sciences","type":"SEO20"}],"international-collaboration":["United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"Real-world data environments contain complex, interconnected information that imparts highly valuable knowledge, such as how different regions in an urban transportation network interact or how financial fraudsters communicate with users. Currently, Australia lacks advanced tools to analyse these intricate connections and discover interpretable patterns, limiting the ability of domain experts to make informed decisions. This project aims to bridge this gap by developing advanced graph-level data mining techniques that will enable an effective, reliable, and robust analysis pipeline. The outcomes will benefit Australians economically and socially. In transportation, these techniques will optimise traffic management by analysing interactions within urban networks, helping to reduce congestion and improve the efficiency of public services. In the financial sector, the project will enhance risk control capabilities, protecting Australian businesses and consumers from fraudulent financial networks and fostering a positive and healthy commercial environment. By advancing capabilities in handling complex graph structures, this project will position Australia as a leader in the rapidly growing global graph analytics market."}}}