Introduction In the paper “On the modelling and impact of negative edges in graph convolutional networks for node classification” (Dinh, Handl and Ospina-Forero(2023)), accepted by NeurIPS 2023 Workshop: New Frontiers in Graph Learning, the authors examine existing Graph Convolutional Network (GCN) frameworks for node classification in signed graphs, focusing on how these frameworks integrate signed edge information and their strengths and weaknesses. The authors conducted …

Decision-making refers to a dynamic process which begins from evidence accumulation to belief adjustment, where belief aggregation is a crucial step in this process. In many decision-making scenarios, individuals may have different beliefs or preferences, and the goal of belief aggregation is to arrive at a consensus or collective decision that represents the overall view of the group. This process can involve various methods such …

Introduction Signed graphs are a type of graph that can simultaneously express positive and negative relationships. These data structures have been receiving increasing attention due to the rising popularity of online social networks. For example, in social graphs, people create positive relationships, such as friendships, trust, and approval, as well as negative relationships, such as foes, distrust and disapproval. Compared to unsigned graphs that only …

In this blog post, I will summarise an article that proposes a modified version of three community detection assessment measures (Purity, Adjusted Rand Index and Normalized Mutual Information). The modified measures include network topological information to assess misclassification errors according to nodes’ integration into the network. This article was published in 2013 in the International Journal of Social Network Mining by Vincent Labatut (Labatut, 2015). …

In this post, I will summarise an article that proposes a redefinition of the clustering coefficients for two-mode networks. The new definition aims to solve some problems that arise from applying, in projected two-mode networks, the clustering coefficient defined in one-mode networks. This article was published in 2013 in the Journal ELSEVIER by Tore Opsahl (Opsahl, 2013). The author introduced the article by explaining some …

In this blog post, I will summarise an article that proposes a new approach to model human mobility. This article was published in 2020 in the Journal Nature by Laura Alessandretti, Ulf Aslak and Sune Lehman (Alessandretti et al., 2020).   The authors started the article by explaining that human mobility is a key issue to understand other phenomena such as people’s commuting flows, money’s …

This blog will review a survey of the applications of complex networks to real-world problems. In particular, six applications related to Social Networks, Economy and Security and Surveillance will be summarised. This article was published in the Journal Advances in Physics, in 2008 by Luciano da Fontoura Costa, Osvaldo N. Oliveira Jr., Gonzalo Travieso, Francisco Aparecido Rodrigues, Paulino Ribeiro Villas Boas, Lucas Antiqueira, Matheus Palhares …

Many important real-world datasets come in the form of graphs or networks: social networks, citation networks, protein-interaction networks, the World Wide Web, etc. The high interpretability of graph and the rise of deep learning has motivated to create a new intersection between deep learning and graph theory. When both these fields meet they create what we call geometric deep learning or graph neural network. It …

Created in 2009, Bitcoin now is the most accepted cryptocurrency in the world and is traded on over 40 exchanges worldwide. Several innovative features of the Bitcoin such as decentralized peer-to-peer payment network without central banks, anonymity and greater accessibility relative to traditional currencies make it appealing to investors and traders. Thus, there is an increasing number of research that study the time series of …

In this post, I will review an article that used adaptative networks to model complex systems in some real-world problems. This article was published in 2013 in the Journal ELSEVIER by Hiroki Sayama, Irene Pestov, Jeffrey Schmidt, Benjamin James Bush, Chun Wong, Junichi Yamanoi and Thilo Gross (Sayama et al., 2013). This article aimed to introduce fundamental concepts and properties of adaptive networks through a …

Time series forecasting plays an important role in various practical applications ranging from energy, electrical load, tourism to finance. Improving forecasting performance is an important yet regularly difficult task. Forecast combination is considered as one effective way to improve the performance of forecasting. With the aim of utilizing Artificial Neural Network (ANN) to improve time series forecasting, the article titled “Optimal forecast combination based on …

Introduction When it comes to financial data, there is a high chance that seasonal patterns will be present there. These are defined as patterns that have cyclic behavior. Let’s assume, that there is a store that sells ice-cream during the whole year. An example of monthly seasonal patterns could be the increased ice-cream sales in that store during the summer period in comparison with the …

This blog post aims to generate forecasts of house property sales using Vector Autoregression (VAR) models. The dataset is downloaded from: https://www.kaggle.com/htagholdings/property-sales?select=ma_lga_12345.csv We utilize “ma_lga_12345.csv” dataset that contains data resampled using Median Price Moving Average (MA) in quarterly intervals. The data range from 30 September 2007 to 30 September 2019 at the time of download (8 July 2020). We focus on predicting the house price …

In this blog post, I will perform a House Property Sales forecast using a Random Forest technique with a Linear Regression and a Time Series. To conduct these models, it was used two databases: The Raw data: 29580 observations of recorded sales data from 2007 to 2019. The MA data: 347 observations of Moving Average of Median Price grouped by quarterly intervals per property type …

This blog post will detail the steps required to begin your journey into web-scraping. Web Scraping can help solve many of the challenges that are faced in an ever-increasing digital word. Some of these challenges include being able to process the vast amount of data online, having a system that can react quickly to this data changing frequently, and making sure that the quality of the …

By Maria Fernanda Ibarra Gutiérrez and Thu Trang Dinh In this blog post, we will describe the database about House Property Sales, which can be downloaded from: https://www.kaggle.com/htagholdings/property-sales?select=raw_sales.csv According to the first Figure, this database describes some characteristics of the property sales into 5 variables and 29,580 observations from the 7th of February 2007 to the 26 of July 2019. This database does not have …

By Maria Fernanda Ibarra Gutiérrez The Coronavirus disease (Covid-19) is a worldwide health problem that according to the World Health Organization (WHO) has spread in 213 countries. Up to the 13th of April 2020, there were 1,807,308 cases around the world according to the Our World in Data database (Ritchie, 2020).   At the current moment, the United States has the higher number of cases …

This post aims to track the spread of COVID-19, also known as 2019 Novel Coronavirus. It is a new respiratory virus first identified in Wuhan in December 2019. According to Centers for Disease Control and Prevention (2020) the virus probably initially emerged from an animal source but now there are many affected cases indicating person-to-person spread occurring. At this time, how easily or sustainably this …

By Maria Fernanda Ibarra Gutiérrez Big Data analysis is a research approach that has been growing in importance to study several aspects of society, as we live surrounded by governmental and private systems, technological devices and social media platforms that gather information from our daily activities, choices, purchases, searches, health patterns and other digital touchpoints. Therefore, there is a large amount of data suitable for …

There are an incredibly large number of resources available to you for learning R, Starting from the R Manual, step by step books, youtube videos, more books, R blogs and so on. Here you will find a list of only some of these resources: Where to get R (Software) You can download R form https://cran.r-project.org/. R-Studio: An R Editor with additional plus, and which provides …