4 citations · 4 across the 4 of their papers we have counts for
4 papers
GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs
Maria Inês Silva, David Aparício, Beatriz Malveiro +2
Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often nece…
Finding NeMo: Fishing in banking networks using network motifs
Xavier Fontes, David Aparício, Maria Inês Silva +3
Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discovering new fraud schemes. This work…
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Catarina Oliveira, João Torres, Maria Inês Silva +3
Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities…
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
Joana Lorenz, Maria Inês Silva, David Aparício +2
Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cry…