Guilherme Sakaji Kido
Papers
1
Total Citations
52
H-Index
1
About
Guilherme Sakaji Kido is a researcher whose work lies at the intersection of online social network analysis, machine learning, and signal processing. His most-cited paper, "Account classification in online social networks with LBCA and wavelets" (2015), has garnered 52 citations, showcasing his early and impactful contributions to the field. In this work, Kido introduced a novel approach combining Local Binary Co-occurrence Analysis (LBCA) with wavelet transforms to classify user accounts in social networks—a method that has proven valuable for detecting automated or malicious behavior. Beyond this, his research explores how computational techniques can uncover patterns in complex social systems, offering tools for understanding user dynamics and network structures. Kido’s work is particularly notable for its interdisciplinary nature, bridging computer science and social science to address pressing issues like misinformation and online security. For students and researchers, his contributions highlight the power of integrating diverse methodologies to solve real-world problems in digital environments.
Research Focus
Key Achievements
Top Papers
- 1Account classification in online social networks with LBCA and wavelets52 citations · 2015