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How to learn R
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 … Continue reading How to learn R
Paper summary: On the modelling and impact of negative edges in Graph Convolutional Netwoks for node classification
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 … Continue reading Paper summary: On the modelling and impact of negative edges in Graph Convolutional Netwoks for node classification
Transparency on the reporting of public procurement information: lessons from handling compiled procurement information
In this blog post, we will summarise the key challenges affecting the transparency of public procurement information in the UK, including data quality issues such as lack of unique identifiers, duplicated records, inconsistent dates, and missing data fields. We argue that improving data collection, quality, and availability in public procurement is important to support accountability, transparency and to inform policy reform. Finally, we will describe … Continue reading Transparency on the reporting of public procurement information: lessons from handling compiled procurement information
Belief aggregation through weighted Dempster’s rule of combination
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 … Continue reading Belief aggregation through weighted Dempster’s rule of combination
Graph Convolutional Networks for node classification in signed graphs-Part 1
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
Ball mapper over bank’s customers.
In this blog post, I will show an R application of a Topological Data Analysis tool called Ball Mapper (BM), to visualise the distribution of the bank’s customers that have stayed or exited the bank across the joint distribution of the customers’ characteristics. BM is a useful tool to visualise datasets with multiple dimensions, to do so, BM summarises points that are close to each … Continue reading Ball mapper over bank’s customers.
Article review: Generalized measures for the evaluation of Community Detection methods.
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.
Multivariate Time Series analysis with volatility-Oil Prices
With the basic analysis on the univariate time series on last blog post “Univariate Time Series Analysis -Oil Prices”. This blog post will continue the analysis on multivariate time series. First is using Henze-Zirklers test to check the multivariate normality. The mvnTest = ”hz” in the mvn function can perform the Henze-Zirklers test. The last column indicates whether data set follows a multivariate normality or … Continue reading Multivariate Time Series analysis with volatility-Oil Prices
Univariate Time Series Analysis -Oil Prices
This blog post will try to modeling and forecasting univariate time series dataset with ARMA-GARCH model and exam the goodness of fit with some basic tests. The oil prices dataset is the log returns of four benchmarks(West Texas Intermediate (WTI), Brent Blend, Dubai Crude and Maya) from 10/1/1997 to 4/6/2010. In this data set, each benchmark contains 698 observations, each of them was divided between … Continue reading Univariate Time Series Analysis -Oil Prices
Article review. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients.
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.
Article review: The scales of human mobility.
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 … Continue reading Article review: The scales of human mobility.
Article review. Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
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 … Continue reading Article review. Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
Introduction to Graph Convolutional Network
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
Analysis of two-mode networks
In this blog post it will be analysed a two-mode network of students’ enrolments into modules at the University. Firstly, it will be shown how to visualise this two-mode network. Secondly, it will be demonstrated how to transform this network into a one-mode network to explore the similarities of each mode. The latter will be made using three methods: Overlaps count, Jaccard Similarity and Simple … Continue reading Analysis of two-mode networks
Article review: Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using Machine Leaning techniques
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 … Continue reading Article review: Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using Machine Leaning techniques
Article review: Modeling complex systems with adaptive networks
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
Article review: Optimal forecast combination based on neural network for time series forecasting
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
The robustness of ARIMA models with respect to parameter estimated and forecasted values
Aims, Objectives & Procedure followed My dissertation aimed in exploring how robust ARIMA (a time series forecasting technique) modelling is when time series data is non-stationary. What’s more, once robustness was explored an overarching framework was created as a supplementary aim.In particular this overarching framework consisted of a s set of rules on how to obtain accurate forecasts through ARIMA modelling when data exhibit certain … Continue reading The robustness of ARIMA models with respect to parameter estimated and forecasted values
Exploring the performance of Neural Network Architectures in the forecast of stock prices
This article presents information about my MSc Dissertation at the University of Manchester. Key Features of the Dissertation The main objective of this dissertation was to investigate the performance of different type of Neural Network Architectures in the forecast of stock prices. The selected data for the research was about Microsoft’s stock price which consisted of the daily Open, Close, Low, High and Volume(OCLHV) and … Continue reading Exploring the performance of Neural Network Architectures in the forecast of stock prices
Introduction to Convolutional Neural Networks
This is the first of two blogposts taking a look at the paper CNNPred: CNN-based stock market prediction using several data sources Ehsan Hoseinzade , Saman Haratizadeh , Faculty of New Sciences and Technologies, University of Tehran which attempts to showcase the application of CNNs to the stock market. In this first blogpost we are going to focus on the basics of convolutional neural networks, more specifically how they are applied … Continue reading Introduction to Convolutional Neural Networks
A Gentle Introduction for Seasonal ARIMAX (SARIMAX)
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)
Data and Methods Exploration Group 