arXiv subjects

Oliver E. Williams

Publications and source records attributed to Oliver E. Williams.

4 recordsLinked to original sources

arXiv2021

Non-Markovian temporal networks with auto- and cross-correlated link dynamics

Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often non-Markovian, and the dynamics of their links correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key. We here introduce a general class of models of temporal networks based on discrete autoregressive processes. As a case study we concentrate on a specific model within this class, generating temporal networks with a specified underlying backbone, and with precise control over the dynamical dependencies between links and the strength and length of their memories. In this network model the presence of each link is influenced by its own past activity and the past activities of other links, as specified by a coupling matrix, which directly controls the causal relations and correlations among links. We propose a method for estimating the models parameters and how to deal with heterogeneity and time-varying patterns, showing how the model allows for a more realistic description of real world temporal networks and also to predict their evolution. We then investigate the role that memory and correlations in link dynamics have on processes occurring over a temporal network by studying the speed of a spreading process, as measured by the time it takes for diffusion to reach equilibrium. Through both numerical simulations and analytical results, we are able to separate the roles of autocorrelations and neighbourhood correlations in link dynamics, showing that the speed of diffusion is non-monotonically dependent on the memory length, and that correlations among neighbouring links can speed up the spreading process, while autocorrelations slow it down.

cond-mat.stat-mech↗

arXiv2020

The shape of memory in temporal networks

Temporal networks are widely used models for describing the architecture of complex systems. Network memory -- that is the dependence of a temporal network's structure on its past -- has been shown to play a prominent role in diffusion, epidemics and other processes occurring over the network, and even to alter its community structure. Recent works have proposed to estimate the length of memory in a temporal network by using high-order Markov models. Here we show that network memory is inherently multidimensional and cannot be meaningfully reduced to a single scalar quantity. Accordingly, we introduce a mathematical framework for defining and efficiently estimating the microscopic shape of memory, which fully characterises how the activity of each link intertwines with the activities of all other links. We validate our methodology on a wide range of synthetic models of temporal networks with tuneable memory, and subsequently study the heterogeneous shapes of memory emerging in various real-world networks.

physics.soc-ph↗

arXiv2019

Quantifying and predicting success in show business

Recent studies in the science of success have shown that the highest-impact works of scientists or artists happen randomly and uniformly over the individual's career. Yet in certain artistic endeavours, such as acting in films and TV, having a job is perhaps the most important achievement: success is simply making a living. By analysing a large online database of information related to films and television we are able to study the success of those working in the entertainment industry. We first support our initial claim, finding that two in three actors are "one-hit wonders". In addition we find that, in agreement with previous works, activity is clustered in hot streaks, and the percentage of careers where individuals are active is unpredictable. However, we also discover that productivity in show business has a range of distinctive features, which are predictable. We unveil the presence of a rich-get-richer mechanism underlying the assignment of jobs, with a Zipf law emerging for total productivity. We find that productivity tends to be highest at the beginning of a career and that the location of the "annus mirabilis" -- the most productive year of an actor -- can indeed be predicted. Based on these stylized signatures we then develop a machine learning method which predicts, with up to 85% accuracy, whether the annus mirabilis of an actor has yet passed or if better days are still to come. Finally, our analysis is performed on both actors and actresses separately, and we reveal measurable and statistically significant differences between these two groups across different metrics, thereby providing compelling evidence of gender bias in show business.

physics.soc-ph↗

arXiv2018

Effects of memory on spreading processes in non-Markovian temporal networks

Many biological, social and man-made systems are better described in terms of temporal networks, i.e. networks whose links are only present at certain points in time, rather than by static ones. In particular, it has been found that non-Markovianity is a necessary ingredient to capture the non-trivial temporal patterns of real-world networks. However, our understanding of how memory can affect the properties of dynamical processes taking place over temporal networks is still very limited, being especially constrained to the case of short-term memory. Here, by introducing a model for temporal networks in which we can precisely control the link density and the strength and length of memory for each link, we unveil the role played by memory on the dynamics of epidemic spreading processes. Surprisingly, we find that the average spreading time in our temporal networks is often non-monotonically dependent on the length of the memory, and that the optimal value of the memory length which maximizes the spreading time depends on the strength of the memory and on the density of links in the network. Through analytical arguments we then explore the effect that changing the number and length of network paths connecting any two nodes has on the value of optimal memory.

physics.soc-ph↗