Temporal Networks: Slowing Down Diffusion by Long Lasting Interactions

The evaluation and assessment of the design of large wireless networks is an important problem in numerous applications. Direct simulation is a traditional approach for studying such networks but is severely limited in its utility as the size of the network increases. This necessitates other means for studying large networks, one of which is the modeling of large networks with continuum models. In this paper, we introduce nonlinear partial differential equations whose solutions approximate the expected behavior of large networks governed by probabilistic communication rules. The relative speed at which solutions can be obtained from the continuum models allows for the investigation of routing protocols and communication limitations due to interference—a feat that is not feasible via simulation methods. Specifically, we investigate the effects of a directed diffusion routing protocol and explore communication limitations in an interference-sensitive network. Network design studies using the approximating continuum models are then presented.

Identifying influential spreaders is an important issue for capturing the dynamics of information diffusion in temporal networks. Most of the identification of influential spreaders in previous researches were focused on analysing static networks, rarely highlighted on dynamics. However, those measures which are proposed for static topologies only, unable to faithfully capture the effect of temporal variations on the importance of nodes. In this paper, a shortest temporal path algorithm is proposed for calculating the minimum time that information interaction between nodes. This algorithm can effectively find out the shortest temporal path when considering the network integrity. On the basis of this, the temporal efficiency centrality (TEC) algorithm in temporal networks is proposed, which identify influential nodes by removing each node and taking the variation of the whole network into consideration at the same time. To evaluate the effectiveness of this algorithm, we conduct the experiment on four real-world temporal networks for Susceptible-Infected-Recovered (SIR) model. By employing the imprecision and the Kendall’s au coefficient, The results show that this algorithm can effectively evaluate the importance of nodes in temporal networks.

Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.

A reliable video quality assessment (VQA) algorithm is essential for evaluating and optimizing video processing pipelines. In this paper, we propose a quality aggregation network (QAN) for full-reference VQA, which models the characteristics of human visual perception of video quality in both spatial and temporal domain. The proposed QAN is composed of two mod-ules, the spatial quality aggregation (SQA) network and the tem-poral quality aggregation (TQA) network. Specifically, the SQA network models the quality of video frames using 3D CNN, taking both spatial and temporal masking effects into consideration for the modeling of the perception of human visual system (HVS). In the TQA network, considering the memory effect of HVS facing the temporal variation of frame-level quality, an LSTM-based temporal quality pooling network is proposed to capture the nonlinearities and temporal dependencies involved in the process of quality evaluation. According to the experimental results on two well-established VQA databases, the proposed model could outperform the state-of-the-art metrics. The code of the proposed method is available at: https://github.com/lorenzowu/QAN.

While the interaction between information and disease in static networks has been extensively investigated, many studies have ignored the characteristics of network evolution. In this study, we construct a new two-layer coupling model to explore the interactions between information and disease. The upper layer describes the diffusion of disease-related information, and the lower layer represents the disease transmission. We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation, revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network. Subsequently, we derive the disease outbreak threshold by using the microscopic Markov-chain approach (MMCA). Finally, we perform extensive Monte Carlo (MC) numerical simulations to verify the accuracy of our theoretical results. Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer. Moreover, reducing the damping factor can delay disease outbreaks and suppress disease transmission, while improving individual quarantine measures can contribute positively to disease control. This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

In complex networks, the “rich-get-richer” effect (nodes with high degree at one point in time gain more degree in their future) is commonly observed. In practice this is often studied on a static network snapshot, for example, a preferential attachment model assumed to explain the more highly connected nodes or a rich-club effect that analyses the most highly connected nodes. In this paper, we consider temporal measures of how success (measured here as node degree) propagates across time. By analogy with social mobility (a measure of people moving within a social hierarchy through their life) we define hierarchical mobility to measure how a node’s propensity to gain degree changes over time. We introduce an associated taxonomy of temporal correlation statistics including mobility, philanthropy and community. Mobility measures the extent to which a node’s degree gain in one time period predicts its degree gain in the next. Philanthropy and community measure similar properties related to node neighbourhood. We apply these statistics both to artificial models and to 26 real temporal networks. We find that most of our networks show a tendency for individual nodes and their neighbourhoods to remain in similar hierarchical positions over time, while most networks show low correlative effects between individuals and their neighbourhoods. Moreover, we show that the mobility taxonomy can discriminate between networks from different fields. We also generate artificial network models to gain intuition about the behaviour and expected range of the statistics. The artificial models show that the opposite of the “rich-get-richer” effect requires the existence of inequality of degree in a network. Overall, we show that measuring the hierarchical mobility of a temporal network is an invaluable resource for discovering its underlying structural dynamics.

Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

In network analysis, the betweenness centrality of a node informally captures\nthe fraction of shortest paths visiting that node. The computation of the\nbetweenness centrality measure is a fundamental task in the analysis of modern\nnetworks, enabling the identification of the most central nodes in such\nnetworks. Additionally to being massive, modern networks also contain\ninformation about the time at which their events occur. Such networks are often\ncalled temporal networks. The temporal information makes the study of the\nbetweenness centrality in temporal networks (i.e., temporal betweenness\ncentrality) much more challenging than in static networks (i.e., networks\nwithout temporal information). Moreover, the exact computation of the temporal\nbetweenness centrality is often impractical on even moderately-sized networks,\ngiven its extremely high computational cost. A natural approach to reduce such\ncomputational cost is to obtain high-quality estimates of the exact values of\nthe temporal betweenness centrality. In this work we present ONBRA, the first\nsampling-based approximation algorithm for estimating the temporal betweenness\ncentrality values of the nodes in a temporal network, providing rigorous\nprobabilistic guarantees on the quality of its output. ONBRA is able to compute\nthe estimates of the temporal betweenness centrality values under two different\noptimality criteria for the shortest paths of the temporal network. In\naddition, ONBRA outputs high-quality estimates with sharp theoretical\nguarantees leveraging on the \\emph{empirical Bernstein bound}, an advanced\nconcentration inequality. Finally, our experimental evaluation shows that ONBRA\nsignificantly reduces the computational resources required by the exact\ncomputation of the temporal betweenness centrality on several real world\nnetworks, while reporting high-quality estimates with rigorous guarantees.\n

The research on node importance identification for temporal networks has attracted much attention. In this work, combined with the multi-layer coupled network analysis method, an optimized supra-adjacency matrix (OSAM) modeling method was proposed. In the process of constructing an optimized super adjacency matrix, the intra-layer relationship matrixes were improved by introducing the edge weight. The inter-layer relationship matrixes were formed by improved similarly and the inter-layer relationship is directional by using the characteristics of directed graphs. The model established by the OSAM method accurately expresses the structure of the temporal network and considers the influence of intra- and inter-layer relationships on the importance of nodes. In addition, an index was calculated by the average of the sum of the eigenvector centrality indices for a node in each layer and the node importance sorted list was obtained from this index to express the global importance of nodes in temporal networks. The experimental results on three real temporal network datasets Enron, Emaildept3, and Workspace showed that compared with the SAM and the SSAM methods, the OSAM method has a faster message propagation rate and larger message coverage and better SIR and NDCG@10 indicators.

Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categories of staff involved in contacts, and the frequency of recurring contacts. It also provides data augmentation by reconstructing contacts for days when some individuals are present in the HCS without having contacts recorded in the empirical data. Recording bias is formalized through an observation model, to allow direct comparison between the augmented and observed networks. We validate our algorithm using data collected during the i-Bird study, and compare the empirical and reconstructed networks. The algorithm was substantially more accurate to reproduce network characteristics than random graphs. The reconstructed networks reproduced well the assortativity by ward (first-third quartiles observed: 0.54-0.64; synthetic: 0.52-0.64) and the hourly staff and patient contact patterns. Importantly, the observed temporal correlation was also well reproduced (0.39-0.50 vs 0.37-0.44), indicating that our algorithm could recreate a realistic temporal structure. The algorithm consistently recreated unobserved contacts to generate full reconstructed networks for the LTCF. To conclude, we propose an approach to generate realistic temporal contact networks and reconstruct unobserved contacts from summary statistics computed using individual-level interaction networks. This could be applied and extended to generate contact networks to other HCS using limited empirical data, to subsequently inform individual-based epidemic models.

Some applications of simulation require that the model state be advanced in simulation time than the wall-clock time advances as the simulation executes. This faster than requirement is crucial, for instance, when a simulation is used as part of a real-time control system, working through the consequences of contemplated control actions, in order to identify feasible (or even optimal) decisions. This paper considers the issue of than real-time simulation of very large communication networks, and how this is accomplished using our implementation (in C++) of the scalable simulation framework (SSF). Our tool (called iSSF) uses hierarchical levels of abstraction, and parallelism, to achieve speedups of nearly four orders of magnitude, enabling real-time execution rates on large network models. We quantify the effects that choice of hierarchical abstraction has on the simulation time advance rate, and show analytically and empirically how changing the abstraction mix affects performance.

Most real networks are characterized by connectivity patterns that evolve in time following complex, non-Markovian, dynamics. Here we investigate the impact of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven networks with and without memory (i.e., Markovian and non-Markovian). We show that while memory inhibits the spreading process in SIR models, where the epidemic threshold is moved to larger values, it plays the opposite effect in the case of the SIS, where the threshold is lowered. The heterogeneity in tie strengths, and the frequent repetition of connections that it entails, allows in fact less virulent SIS-like diseases to survive in tightly connected local clusters that serve as reservoir for the virus. We validate this picture by evaluating the threshold of both processes in a real temporal network. Our findings confirm the important role played by non-Markovian network dynamics on dynamical processes

Modular structure is a typical structure that is observed in most of real networks. Diffusion dynamics in network is getting much attention because of dramatic increasing of the data flows via the www. The diffusion dynamics in network have been well analysed as probabilistic process, but the proposed frameworks still shows the difference from the real observations. In this paper, we analysed spectral properties of the networks and diffusion dynamics. Especially, we focus on studying the relationship between modularity and diffusion dynamics. Our analysis as well as simulation results show that the relative influences from the non-largest eigenvalues and the corresponding eigenvectors increase when modularity of network increases. These results have the implication that, although network dynamics have been often analysed with the approximation manner utilizing only the largest eigenvalue, the consideration of the other eigenvalues is necessary for the analysis of the network dynamics on real networks. We also investigated Node-level Eigenvalue Influence Index (NEII) which can quantify the relative influence from each eigenvalues on each node. This investigation indicates that the influence from each eigenvalue is confined within the modular structures in the network. These findings should be made consideration by researchers interested in diffusion dynamics analysis on real networks for deeper analysis.

Skeleton-based action recognition networks have widely adopted the approach of Graph Convolutional Networks (GCN) due to their superior capabilities in modeling data topology, but several key issues still require further investigation. Firstly, the graph convolutional network extracts action features by applying temporal convolution to each key point, which causes the model to ignore the temporal connections between different important points. Secondly, the local receptive field of graph convolutional networks limits their ability to capture correlations between non-adjacent joints. Motivated by the State Space Model (SSM), we propose an Action Spatio-temporal Aggregation Network, named ActionMamba. Specifically, we introduce a novel embedding module called the Action Characteristic Encoder (ACE), which enhances the coupling of temporal and spatial information in skeletal features by combining intrinsic spatio-temporal encoding with extrinsic space encoding. Additionally, we design an Action Perception Model (APM) based on Mamba and GCN. By effectively combining the excellent feature processing capabilities of GCN with the outstanding global information modeling capabilities of Mamba, APM is able to comprehend the hidden features between different joints and selectively filter information from various joints. Extensive experimental results demonstrate that ActionMamba achieves highly competitive performance on three challenging benchmark datasets: NTU-RGB+D 60, NTU-RGB+D 120, and UAV–Human.

Many carbonate rocks have pore features that are large at the core plug scale. Laboratory assessment of recovery behavior in such carbonates can be unreliable. We review 3-d Computed Tomography (CT) images of the porosity distribution and of multi-rate waterfloods on a dolomite sample. These clearly show conventional analysis is inadequate, and we investigate use of large pore network models to improve our evaluation. Through parametric studies, we determine whether our large pore network models contain characteristics observed in the core floods. We then calibrate our models to laboratory inputs to improve quantitative interpretation. The model inputs are distributions of pore body and throat radii, throat length, coordination number, wettability and degree of local and spatial correlation. The laboratory inputs are porosity, permeability, thin section analyses, scanning electron microscopy images, mercury injection, and centrifuge capillary pressure. Finally we compare model predictions of miscible and water flood behavior to experimental data. Introduction Carbonate rocks often display heterogeneities significant at the core plug scale. Conventional analysis is likely to be inadequate, and it is not surprising that the literature contains few references to multiphase flow studies on such rocks. A review of studies on carbonate systems by Espie et al. shows a wide range of process efficiency. They find that the residual oil saturation to water flood varies from 28 to 80% oip and that to tertiary miscible flood varies from 0 to 50% oip. Since the pioneering work of Fatt to simulate flow in porous media by a mesh of tubes, many such pore network models have been published. These models typically simulate 2-dimensional flow and have usually been of limited size. However, Lee et al. have recently implemented a 3-d half-million node model on massively parallel computers. This development, coupled with the increased application of 3-d flow visualization to laboratory corefloods, provided the impetus for the present work. Laboratory Studies on Heterogeneous Carbonates Archer and Wong found that conventional relative permeability calculation methods were inadequate for their experiments on a vugular, full diameter carbonate sample. Early water breakthrough had a strong effect on the relative permeability calculations, and the water relative permeability, krw, showed anomalous humps and plateaus. Ehrlich measured drainage relative permeability for vugular, low-permeability samples from the Westerose reservoir. He used the steady state method and full diameter cores. These relative permeability curves also showed significant plateau regions. MacAllister et al. performed steady-state, water-displacing-oil tests on a mixed-wet Baker dolomite sample (k =110 md, =22%) using pressure drops of 4 psi and 100 psi. Both krw and kro were significantly lower at 100 psi. In the 4 psi case, CT images showed that oil and water flow occurred through separate macroscopic regions. This resulted in high relative permeability to both phases. In the 100 psi case, the saturation was more uniformly distributed and the relative permeability values were lower. Narayanan and Deans studied miscible xenon-helium displacement in a full diameter, vugular carbonate sample from Gulf-Connor Fenn-Big Valley, Canada. They concluded that centimeter-scale heterogeneities control flow distribution and that laboratory miscible flood behavior was dominated by convection. P. 61