preface

My journey into the world of graphs began unexpectedly, during an interview at LinkedIn. As the session wrapped up, I was shown a visualization of my network—a mesmerizing structure that told stories without a single word. Organizations I had been part of appeared clustered, like constellations against a dark canvas. What surprised me most was that this structure was not built using metadata LinkedIn held about my connections; rather, it emerged organically from the relationships between nodes and edges.

Years later, driven by curiosity, I recreated that visualization. I marveled once again at how the underlying connections alone could map out an intricate picture of my professional life. This deepened my appreciation for the power inherent in graphs—a fascination that only grew when I joined Cloudera and encountered graph neural networks (GNNs). Their potential for solving complex problems was captivating, but diving into them was like trying to navigate an uncharted forest without a map. There were no comprehensive resources tailored for nonacademics; progress was slow, often cobbled together from fragments and trial and error.