Design of vertex-centric algorithms for pattern recognition on large-scale graphs using asynchronous parallel processing

• Grant number: 14/25337-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2016
• End date: February 28, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: José Fernando Rodrigues Júnior
• Grantee: Gabriel Perri Gimenes
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Since the birth of web 2.0, users no longer just consume, but are now active creators of content that is going to be consumed by other users. This new dynamic took data generation to a whole new scale, called planetary scale or web scale. Often, this data represents relationships between its elements, such as in social networks, recommendation systems, online boards, email networks, scientific citation networks and others. Therefore, this data can be properly modeled as graphs, which contain properties like weights on its edges, associated texts and dynamic behavior that can be explored. To analyze such graphs, the main approach consists of using distributed processing techniques via computer clusters leading to high costs and techinical complexity that can be prohibitive. So, it is desirable to be able to process planetary scale graphs using only a single computer. To do this, we intend to combine edge and vertex centric iterative processing with discrete matrix processing and text processing techniques aiming to develop an analysis framework capable of recognizing patterns, comprehension and helping with decision making. With such methods we intend to develop new algorithms and systems to solve problems like fraud detection, behavior analysis and sentiment analysis, in a variety of domains.

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SPADON, GABRIEL; DE CARVALHO, ANDRE C. P. L. F.; RODRIGUES-JR, JOSE F.; ALVES, LUIZ G. A.. Reconstructing commuters network using machine learning and urban indicators. SCIENTIFIC REPORTS, v. 9, AUG 13 2019. (16/17078-0, 17/08376-0, 19/04461-9, 13/07375-0, 16/16987-7, 16/18615-0, 14/25337-0)

BRANDOLI, BRUNO; DE GEUS, ANDRE R.; SOUZA, JEFFERSON R.; SPADON, GABRIEL; SOARES, AMILCAR; RODRIGUES, JR., JOSE F.; KOMOROWSKI, JERZY; MATWIN, STAN. Aircraft Fuselage Corrosion Detection Using Artificial Intelligence. SENSORS, v. 21, n. 12, JUN 2021. (17/08376-0, 18/17620-5, 19/04461-9, 20/07200-9, 16/17078-0, 14/25337-0)

SPADON, GABRIEL; HONG, SHENDA; BRANDOLI, BRUNO; MATWIN, STAN; RODRIGUES-JR, JOSE F.; SUN, JIMENG. Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v. 44, n. 9, p. 17-pg., 2022-09-01. (16/17078-0, 20/07200-9, 18/17620-5, 17/08376-0, 14/25337-0, 19/04461-9)

SPADON, GABRIEL; GIMENES, GABRIEL; RODRIGUES JR, JOSE F.; SHI, Y; FU, H; TIAN, Y; KRZHIZHANOVSKAYA, VV; LEES, MH; DONGARRA, J; SLOOT, PMA. Topological Street-Network Characterization Through Feature-Vector and Cluster Analysis. COMPUTATIONAL SCIENCE - ICCS 2018, PT I, v. 10860, p. 14-pg., 2018-01-01. (16/17078-0, 17/08376-0, 14/25337-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

GIMENES, Gabriel Perri. Data analysis over large-scale graphs using vertex-centric asynchronous parallel processing. 2020. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.