Friday, September 13
2:35pm in Wege Auditorium (TCL 123)
Giving your Graph a Voice: Graph Representations and Large Language Models
Graphs are powerful tools for representing complex real-world relationships, essential for tasks like analyzing social networks or identifying financial trends. While large language models (LLMs) have revolutionized natural text reasoning, their application to graphs remains an understudied frontier. To bridge this gap, we need to transform structured graph data into representations LLMs can process. This talk delves into our work on finding the correct graph inductive bias for Graph ML and developing strategies to convert graphs into language-like formats for LLMs. I’ll explore our work on “Talking Like a Graph”, and our parameter-efficient method, GraphToken, which learns an encoding function to extend prompts with explicit structured information.
Bryan Perozzi is a Research Scientist in Google Research’s Algorithms and Optimization group, whose research focuses on learning expressive representations of graph data with neural networks. Bryan is an author of 40+ peer-reviewed papers at leading conferences in machine learning and data mining (such as NeurIPS, ICML, ICLR, KDD, and WWW). He’s the author of popular models in graph representation learning such as DeepWalk (random walk node embeddings), MixHops (graph neural networks) and more. Bryan’s current research focuses on the intersection of structured data and generative AI, where he’s teaching large language models to ‘Talk Like a Graph’.
Bryan was awarded the ACM SIGKDD 2024 Test of Time Award for his work in advancing neural network representations for graph data in “DeepWalk: Online Learning of Social Representations”, and his doctoral thesis won the prestigious ACM SIGKDD Dissertation Award (2017). Bryan received his Ph.D. in Computer Science from Stony Brook University, where he was advised by Steven Skiena. Prior to that he obtained a M.S. in Computer Science from The Johns Hopkins University and a B.S. in Computer Engineering from Virginia Tech.