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Graph Machine Learning with Python pt. 1: Basics, Metrics, and Algorithms

#artificialintelligence

Networks also have some basic properties that advanced methods and techniques build upon. There are numerous datasets with a preloaded network structure available to do work on. This is due to the graciousness of the research and applied community sharing their work and datasets. To get started with our own network, we can load in one of these NetworkX's datasets and as a sports fan I'll choose the Football dataset (M.


Topological based classification of paper domains using graph convolutional networks

arXiv.org Machine Learning

The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their class to predict this class. Moreover, combining topological information with information propagation improves classification accuracy on the standard CiteSeer and Cora paper classification task. Topological features and information propagation produce results almost as good as text-based classification, without no textual or content information. We propose to represent the topology and information propagation through a GCN with the neighboring training node classification as an input and the current node classification as output. Such a formalism outperforms state of the art methods.


Knowing Your Neighbours: Machine Learning on Graphs

#artificialintelligence

We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems, and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. The nodes represent research papers, while the edges illustrate citations between papers, with the various colour indicative of a report's subject, with seven colours coding seven topics. Representing connected data is possible using a graph data structure regularly used in Computer Science.


Knowing Your Neighbours: Machine Learning on Graphs - KDnuggets

#artificialintelligence

We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems, and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. The nodes represent research papers, while the edges illustrate citations between papers, with the various colour indicative of a report's subject, with seven colours coding seven topics. Representing connected data is possible using a graph data structure regularly used in Computer Science.


Applications of Graph Neural Networks – Towards Data Science

#artificialintelligence

Graphs and their study have received a lot of attention since ages due to their ability of representing the real world in a fashion that can be analysed objectively. Indeed, graphs can be used to represent a lot of useful, real world datasets such as social networks, web link data, molecular structures, geographical maps, etc. Apart from these cases which have a natural structure to them, non-structured data such as images and text can also be modelled in the form of graphs in order to perform graph analysis on them. Due to the expressiveness of graphs and a tremendous increase in the available computational power in recent times, a good amount of attention has been directed towards the machine learning way of analysing graphs, i.e. According to this paper, Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. They are extensions of the neural network model to capture the information represented as graphs.