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fundamental to the study of graphs is the adjacency matrix which is a data structure in the form of a matrix that encodes all of the connections in the graph between nodes and you can use the adjacency matrix to implement basic forms of the message passing algorithm which is a key concept in understanding many of the modern graph neural network techniques so this video is going to define these terms and show some simple examples using just python to help make the concepts crystal clear [Applause] [Music] before moving on let me mention i have a mailing list where i make announcements about new content either blog posts or videos and i also manage a discord server where theres a community of people like you trying to learn machine learning and its a great place to ask questions so please join us and links to both of those are in the description below lets build the adjacency matrix of a simple example heres a small graph nodes one two and three are connected and a chain and then no
