Seminar za veštačku inteligenciju, 6. novembar 2024.

Naredni sastanak Seminara biće održan onlajn u sredu, 6. novembra 2024. godine, sa početkom u 19 časova.
 
Predavač: Jovan Blanuša, IBM Research Europe - Zurich, Switzerland
 
Naslov predavanja: EFFICIENT MACHINE LEARNING OVER RELATIONAL DATA

Apstrakt:
Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For instance, finding cycles in financial graphs enables the detection of financial crimes such as money laundering and circular stock trading. In addition, extracting cliques from social network graphs enables the detection of communities and could help predict the spread of epidemics. However, extracting such patterns can be time-consuming, especially in larger graphs, because the number of patterns can grow exponentially with the graph size. Therefore, fast and scalable parallel algorithms are required to make the enumeration of these subgraph patterns tractable for real-world graphs.
In this talk, we will first talk about how to efficiently implement algorithms for mining cliques and cycles in graphs.Then, we introduce Graph Feature Preprocessor, which leverages the developed fast graph pattern mining algorithms to expand the feature set of financial transactions by enumerating well-known money laundering and financial fraud subgraph patterns. When used in combination with gradient-boosting-based machine learning models, the expanded feature set produced by the library enables significant improvements in prediction accuracy for the problems of money laundering and phishing detection. Furthermore, the efficiency of the underlying graph pattern mining algorithms  enables this library to operate in real time.

Napomena:
Registraciona forma za učešće i link za aktivno praćenje predavanja za registrovane korisnike (nakon logovanja):
https://miteam.mi.sanu.ac.rs/asset/CW5nJWDSEZDj7p32p
 
Neregistrovani korisnici mogu da prate predavanja na ovom linku (bez mogućnosti aktivnog učešća):
https://miteam.mi.sanu.ac.rs/call/hR9vL94nD6QE8qQZj/xET9GcPMyR08nqH8lnS3SE7N5Vf00H7Lp9EBhsv6Lti


 Ostavite vaš komentar 

Komentari(0)

Nažalost nije moguće ostaviti komentar.