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

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

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

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