QSearchNet: A Quantum Walk Search Framework for Link Prediction
This addresses link prediction for complex systems like social and biological networks, but it is incremental as it builds on existing quantum-inspired methods.
The paper tackles the link prediction problem in graphs by introducing QSearchNet, a quantum-inspired framework that uses quantum walk dynamics and Grover's amplification to integrate local and global structural information, achieving competitive performance on real-world networks.
Link prediction is one of the fundamental problems in graph theory, critical for understanding and forecasting the evolution of complex systems like social and biological networks. While classical heuristics capture certain aspects of graph topology, they often struggle to optimally integrate local and global structural information or adapt to complex dependencies. Quantum computing offers a powerful alternative by leveraging superposition for simultaneous multi-path exploration and interference-driven integration of both local and global graph features. In this work, we introduce QSearchNet, a quantum-inspired framework based on Discrete-Time Quantum Walk (DTQW) dynamics and Grover's amplitude amplification. QSearchNet simulates a topology-aware quantum evolution to propagate amplitudes across multiple nodes simultaneously. By aligning interference patterns through quantum reflection and oracle-like phase-flip operation, it adaptively prioritizes multi-hop dependencies and amplifies structurally relevant paths corresponding to potential connections. Experiments on diverse real-world networks demonstrate competitive performance, particularly with hard negative samples under realistic evaluation conditions.