EventConnector: Mining Social Event Relations through Temporal Graphs

Understanding and retrieving related real-world events based on their temporal dynamics is a fundamental challenge in time-sensitive applications such as forecasting, information retrieval, and social analysis. Existing methods often rely on semantic similarity or global time-series alignment, which overlook the transient and directional dependencies that frequently underlie real-world correlations. In this work, we introduce $\mathit{\text{EventConnector}}$, a framework that constructs a temporal event graph capturing localized co-fluctuations and lead-lag relationships between events through their time-series trajectories. We further propose $\mathbf{\text{EC-Fusion}}$, an adaptive retrieval mechanism that fuses EventConnector’s graph-based scores with a complementary Granger-causal signal via a graph-quality-aware mixing weight. Across two real-world prediction market benchmarks (Polymarket and Kalshi) and nine forecasting architectures evaluated over three random seeds, EC-Fusion is the best non-oracle retrieval method on $17\mathrm{/}18$ model–dataset cells, reducing RMSE by $6.87\mathrm{\%}$ on average (up to $10.86\mathrm{\%}$) over the strongest comparable retrieval baseline, with statistical significance at $p<0.01$ after Holm–Bonferroni correction. These results highlight the effectiveness of temporally grounded graph modeling, augmented with causal-signal fusion, in capturing latent event relationships beyond what semantic similarity or traditional alignment techniques can offer.