Abstract

Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.

Highlights

  • We are currently in the era of big data

  • We propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks

  • We propose two efficient event detection methods for temporal social networks using a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm

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Summary

INTRODUCTION

We are currently in the era of big data. From fundamental physics and biology to the social sciences, almost every field involves very large datasets. Rosvall et al. developed the second-order Markov model to study community detection, ranking, and spreading analysis based on random walks. In this way, they captured actual travel patterns in air traffic and uncovered multidisciplinary journals in scientific communication. Xu et al. revealed the data from many complex systems that can show up to fifth-order dependencies and proposed a higher-order network representation to improve the accuracy of random walking, clustering, and ranking. Lambiotte et al. developed advanced graph modeling and representation techniques based on higher- and variable-order Markov models to describe non-Markovian characteristics of temporal networks. New higher-order network representations of sequential data in complex systems might have the ability to improve the accuracy of event detection. The experiments in empirical networks demonstrate the validity of our methods

Multivariate sequential data
Recovery algorithm
Innovation algorithm
Higher-order network model
Event detection
Data descriptions and measurements
Event detection in the Enron network
Event detection in human contact networks
Result analysis
Node type and node number for network distance
New edge for network distance
DISCUSSIONS
CONCLUSIONS AND PERSPECTIVES
Full Text
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