Inferring Temporal Dependencies from Social Time Series with the Cross-Correlogram

2026-09-15

AI Summary

研究通过改进的交叉互相关图方法，解决了社会时间序列中非平稳和突发行为导致的时间依赖性难以测量的问题。

Abstract

Characterizing temporal interactions in social systems is challenging because social behavior can be bursty and non-stationary, violating the stationarity assumptions of many methods used to measure temporal dependence. The cross-correlogram, an existing technique used to profile neural excitations and inhibitions, offers an interpretable alternative to methods such as Granger causality or co-occurrence, as it produces a full profile of lagged dependence directly from event times rather than a single summary statistic. We adapt the cross-correlogram by integrating functional models of behavior with data-driven temporal response profiling. By characterizing how periodic structure biases traditional cross-correlograms, we propose a correction based on smooth intensity functions, specified from a known functional form or estimated empirically. This approach provides a robust, interpretable estimator of temporal dependency profiles even when collective rhythms operate on timescales that overlap those of the interactions of interest. We demonstrate theoretically and through simulation that the proposed method recovers temporal dependencies in periodic regimes, outperforming interval-jitter and Granger causality methods. Finally, we apply the method to 3.1 million event times from X (formerly Twitter) collected between 2019 and 2020, demonstrating how cross-correlograms reveal delayed temporal relationships in collective online behavior that are missed by co-occurrence measures. For a subset of television-related hashtags, recovered delays align with known broadcast schedules, providing evidence that the proposed method captures genuine temporal structure rather than artifacts of shared attention cycles.

Problem

• temporal dependencies
• social time series
• non-stationary
• cross-correlogram
• periodic structure

Innovation

• Cross-Correlogram
• Temporal Dependencies
• Non-Stationary Social Behavior
• Smooth Intensity Functions
• Periodic Structure Correction