Leandro Anghinoni- Phd Candidate in Computer Science
- PhD Candidate at University of São Paulo
Leandro Anghinoni
- Phd Candidate in Computer Science
- PhD Candidate at University of São Paulo
I currently research methods related to the structure of complex networks and its evolution through time.
About
8
Publications
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Introduction
I am currently working on methods based on networks´ mesostructure, such as communities and core-periphery structures. I also study these structures patterns over time (evaluating concept drifts) and their hierarchy.
Current institution
Education
March 2017 - November 2018
March 2017 - November 2018
February 2000 - November 2006
Publications
Publications (8)
In this study, we investigate the COVID-19 epidemics in Brazilian cities, using early-time approximations of the SIR model in networks and combining the VAR (vector autoregressive) model with machine learning techniques. Different from other works, the underlying network was constructed by inputting real-world data on local COVID-19 cases reported...
Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification...
Identifying time series patterns is of great importance for many real-world problems in a variety of scientific fields. Here, we present a method to identify time series patterns in multiscale levels based on the hierarchical community representation in a complex network. The construction method transforms the time series into a network according t...
Although COVID-19 has spread almost all over the world, social isolation is still a controversial public health policy and governments of many countries still doubt its level of effectiveness. This situation can create deadlocks in places where there is a discrepancy among municipal, state and federal policies. The exponential increase of the numbe...
Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable)...
Extracting knowledge from time series provides important tools for many real applications. However, many challenging problems still open due to the stochastic nature of large amount of time series. Considering this scenario, new data mining and machine learning techniques have continuously developed. In this paper, we study time series based on its...
