Graph Learning

In data-scarce domains, the bottleneck isn’t computing power — it’s labeled data. Semi-supervised learning (SSL) thrives here, using a small set of labeled points to guide a vast sea of unlabeled ones. But what happens when we bring quantum mechanics into the loop? This is exactly where Improved Laplacian Quantum Semi-Supervised Learning (ILQSSL) and Improved Poisson Quantum Semi-Supervised Learning (IPQSSL) enter the stage. From Graphs to Quantum States Both ILQSSL and IPQSSL operate in the graph-based SSL paradigm, where data points become nodes and similarity measures define edges. The twist is how they embed this graph structure directly into quantum states using QR decomposition. By decomposing graph-derived matrices into orthogonal (unitary-compatible) components, they map structure into quantum circuits without violating the unitarity constraints of quantum computing. ...

In the escalating arms race between fraudsters and detection systems, recent advances in Graph-Enhanced LLMs hold enormous promise. But they face a chronic problem: too much information. Take graph-based fraud detection. It’s common to represent users and their actions as nodes and edges on a heterogeneous graph, where each node may contain rich textual data (like reviews) and structured features (like ratings). To classify whether a node (e.g., a user review) is fraudulent, models like GraphGPT or HiGPT transform local neighborhoods into long textual prompts. But here’s the catch: real-world graphs are dense. Even two hops away, the neighborhood can balloon to millions of tokens. ...