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 … Continue reading Graph Convolutional Networks for node classification in signed graphs-Part 1

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). … Continue reading Article review: Generalized measures for the evaluation of Community Detection methods.

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 … Continue reading Article review. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients.

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 … Continue reading Introduction to Graph Convolutional Network

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 … Continue reading Article review: Modeling complex systems with adaptive networks

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 … Continue reading Article review: Optimal forecast combination based on neural network for time series forecasting

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 … Continue reading A Gentle Introduction for Seasonal ARIMAX (SARIMAX)

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 … Continue reading Vector Autoregression (VAR) for house property sales time series