Social Event Prediction via Fourier Graph Learning

Social event prediction has garnered increasing attention in web-centered society. Most existing studies represent web-based event stream as chronological graph sequences, then leverage RNNs and GNNs to model temporal and relational patterns. However, this paradigm is inherently flawed: (1) RNNs struggle to capture long-term temporal dependencies, ignoring those temporally distant but influential events. (2) Spatio-temporal GNNs exhibit high computational complexity on large-scale real-time event streams, which hinders their web applications. To this end, we explore a novel paradigm called Fourier Graph Learning from the perspective of frequency domain. Specifically, we first define a novel data structure called Fourier Graph (FG). In FG, both nodes and edges are complex vectors, with real part encoding semantics and imaginary part representing semantic-specific temporal patterns. These temporal patterns are obtained by semantic-aware frequency filter, which utilizes semantics as guidance to adaptively incorporates both long-term dependency and short-term dynamic. Based on FG, we further propose Fourier Graph Neural Network (FGNN). It replaces time-domain convolution with frequency-domain multiplication for efficient aggregation. FGNN also includes a complex-valued event decoder, which fully leverages semantics and temporal patterns from complex space to predict future event probabilities. Extensive experiments show our superior performance with higher accuracy, less complexity and better interpretability compared with baselines.