Topological–temporal properties of evolving networks

Ceria, Alberto; Havlin, Shlomo; Hanjalic, Alan; Wang, Huijuan

doi:10.1093/comnet/cnac041

Cited by 3 publications

(5 citation statements)

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“…Our findings may shed light on the modeling of the formation of temporal networks which is crucial in understanding and controlling the dynamics of and on temporal networks. Our finding that activities of neighboring links that form a triangle with a target link have prediction power on the connection of the target link may suggest that higher-order events [37,38] like triangles in each network snapshot may contribute to the prediction of (pairwise and higher-order) temporal networks.…”

Section: Discussionmentioning

confidence: 90%

Short- and long-term temporal network prediction based on network memory

Zou

Ceria

Wang

2023

Preprint

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Temporal networks like physical contact networks are networks whose topology changes over time. Predicting future temporal networks is crucial e.g., to forecast and mitigate the spread of epidemics and misinformation on the network. The classic temporal network prediction problem that aims to predict the temporal network in the short-term future based on the network observed in the past has been addressed mostly via machine learning algorithms, at the expense of high computational costs and limited interpretation of the underlying mechanisms that form the networks. This motivates us to develop network-based models to predict future network based on the network properties of links observed in the past. Firstly, we explore the similarity between the network topologies (snapshots) at any two time steps with a given time lag/interval. We find that the similarity is relatively high when the time lag is small and decreases as the time lag increases. Inspired by such time-decaying memory of temporal networks and recent advances, we propose two models that predict a link’s activity (i.e., connected or not) at the next time step based on past activities of the link itself or also of the neighboring links, respectively. It is observed that in seven real-world physical contact networks, our models outperform in both prediction quality and computational complexity, and predict better in networks that have a stronger memory. We also reveal how different types of neighboring links contribute to the prediction of a given link’s future activity, again depending on the properties of temporal networks. Furthermore, we adopt both models as well as baseline models for long-term temporal network prediction, that is, predicting temporal networks multi-time steps ahead based on the network topology observed in the past. Our models still perform better than baseline models at each step ahead in long-term prediction, and our models have a higher prediction quality in networks with stronger memory. The prediction quality of our SD model decays as the prediction step is further ahead in time and this decay speed is positively correlated with the decay speed of network memory. Finally, networks predicted by various models respectively have a heterogeneous distribution of inter-event time similar to real-world networks, and also the burstiness of inter-event times for individual link

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Section: Discussionmentioning

confidence: 90%

Short- and long-term temporal network prediction based on network memory

Zou

Ceria

Wang

2023

Preprint

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“…To detect non-trivial temporal and topological patterns of events, we compare properties obtained from real-world higher-order temporal networks with those of designed null models. We generalize the randomized reference models of pairwise evolving networks which gradually preserve and destroy temporal and topological properties of pairwise interactions 25 – 27 for higher-order temporal networks. Given a higher-order evolving network and any given order d of events, we introduce 3 randomized null models , and which systematically randomize order d events only, without changing events of any other order .…”

Section: Definitionsmentioning

confidence: 99%

“…Such periods usually correspond, e.g., to night and weekends, and are recognized as outliers in the inter-event time distribution of the time series which records the total number of events per timestamp. Such data pre-processing method has also been used in our recent work 25 . The other 5 higher-order collaborations networks are obtained based on scientific papers recorded in the arxiv in various fields: lattice high energy physics (hep-lat), theoretical nuclear physics (nucl-th), quantitative biology (q-bio), quantitative finance (q-fin) and quantum physics (quant-ph).…”

Section: Datasetsmentioning

confidence: 99%

“…Specifically, we aim to understand to what extent events close in time are also close in topology. In our previous work 25 , we considered all interactions in a temporal network as pairwise interactions alone and found in real-world physical and virtual contact networks that pairwise interactions that are close in time tend to be close in topology (in the pairwise time aggregated network). Here, we generalize the method of characterizing the relation between topological and temporal distance of two dyadic interactions to that of two higher-order events with different orders.…”

Section: Characterizing Temporal-topological Properties Of Networkmentioning

confidence: 99%

“…Hence, this work aims to systematically characterize the relation between temporal and topological properties of higher-order events to compare higher-order temporal networks. Inspired by our recent work that characterizes temporal and topological properties of dyadic interactions in temporal networks 25 , we redesign the characterization method for higher-order events. In particular, we are going to explore such properties from three perspectives: (1) The interrelation between the distance in topology and the temporal delay of events, (2) Their correlation or overlap in topological location.…”

Section: Introductionmentioning

confidence: 99%

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Temporal-topological properties of higher-order evolving networks

Ceria

Wang

2023

Sci Rep

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Human social interactions are typically recorded as time-specific dyadic interactions, and represented as evolving (temporal) networks, where links are activated/deactivated over time. However, individuals can interact in groups of more than two people. Such group interactions can be represented as higher-order events of an evolving network. Here, we propose methods to characterize the temporal-topological properties of higher-order events to compare networks and identify their (dis)similarities. We analyzed 8 real-world physical contact networks, finding the following: (a) Events of different orders close in time tend to be also close in topology; (b) Nodes participating in many different groups (events) of a given order tend to involve in many different groups (events) of another order; Thus, individuals tend to be consistently active or inactive in events across orders; (c) Local events that are close in topology are correlated in time, supporting observation (a). Differently, in 5 collaboration networks, observation (a) is almost absent; Consistently, no evident temporal correlation of local events has been observed in collaboration networks. Such differences between the two classes of networks may be explained by the fact that physical contacts are proximity based, in contrast to collaboration networks. Our methods may facilitate the investigation of how properties of higher-order events affect dynamic processes unfolding on them and possibly inspire the development of more refined models of higher-order time-varying networks.

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