Modeling sequences and temporal networks with dynamic community structures

Abstract

In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.

15 Pages, 6 figures, 2 tables

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• When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks
• Randomized reference models for temporal networks
• Detection of timescales in evolving complex systems
• Predictability of real temporal networks
• Different approaches to community detection
• Tensorial and bipartite block models for link prediction in layered networks and temporal networks
• Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem
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• Flow stability for dynamic community detection
• HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks
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• Dynamic Hidden-Variable Network Models
• Understanding Complex Systems: From Networks to Optimal Higher-Order Models
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• State aggregations in Markov chains and block models of networks
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• Convex skeletons of complex networks
• Hidden dependence of spreading vulnerability on topological complexity
• Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
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• Network compression with configuration models and the minimum description length
• Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions
• Bayesian Modeling of Random Walker for Community Detection in Networks
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