Sampling of temporal networks: Methods and biases

Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

doi:10.1103/physreve.96.052302

Cited by 32 publications

(20 citation statements)

References 55 publications

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“…Then, we can employ several of the available tools developed so far to analyze the sampled network and draw conclusions to the original large-scale network. In 32 , the authors presented various strategies of temporal network sampling and quantified the biases generated by each sampling strategy on a number of temporal network measures, such as link activity, temporal paths and epidemic spread.…”

Section: Research Problemmentioning

confidence: 99%

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Temporal Network Pattern Identification by Community Modelling

Gao

Zheng

Vega-Oliveros

et al. 2020

Sci Rep

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Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable) state of a given temporal network as a community in a sampled static network and the temporal state change is represented by the transition from one community to another. For this purpose, a reduced static single-layer network, called a target network, is constructed by sampling and rearranging the original temporal network. Our approach provides a general way not only for temporal networks but also for data stream mining in topological space. Simulation results on artificial and real temporal networks show that the proposed method can group different temporal states into different communities with a very reduced amount of sampled nodes. In real-world applications, data often arrives in streams and must be analyzed in real-time. Patterns and relations in such data are usually not stable but evolve over time 1,2. In dynamical and non-stationary environments, the data distribution can change, yielding the phenomenon of concept drift 2,3. One example is the stock market, where the massive data exchange is strongly related to macro and micro political events, economic events and other factors, such as natural and man-made disasters. Another example is the pattern of customersâ€ ™ buying preferences, which may depend on the season, availability, inflation rate, etc. Some common types of changes may include a gradual change over time, a recurring or cyclical change, and a sudden or abrupt change. Different concept drift detection and handling schemes may be required for each situation. In this context, uncovering data stream patterns is a fundamental task not only to detect concept drift but also to predict future behaviors. Recently, some works have employed networks as a way for representing data from many real systems 4-7 , leading to complex network research. Complex network refers to large scale graphs with nontrivial connection patterns 8-11. One of the most important features of a complex network is the presence of communities. Detecting communities in these systems has become a fundamental task to help us understand how local patterns (represented by sub-networks) interact and produce global behavior. From a network topology point of view, a community is a sub-graph in which the inner links are dense while the outer connections are relatively sparse 4,12,13. In terms of the information-theoretic field, a community is seen as the module that can diminish or retard the propagation flow of information in the system, for a considerable period of time 14-16. In the machine learning domain, community detection provides new unsupervised learning methods, where each community corresponds to a data cluster in the clustering problem 4. Large systems usually are high-dimensional...

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Section: Research Problemmentioning

confidence: 99%

“…Since the target network just needs to capture the temporal changing pattern of the original network, our sampling method is straightforward and, therefore, the minimization of biases between the sampled network and the original temporal network is no longer the objective 32 .…”

Section: Research Problemmentioning

confidence: 99%

Temporal Network Pattern Identification by Community Modelling

Gao

Zheng

Vega-Oliveros

et al. 2020

Sci Rep

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“…Animal contact networks are critical for describing the interactions that underlie pathogen transmission, gene flow, and information transfer, but empirical descriptions of contact are difficult to generate and sometimes biased [1][2][3][4]. Ecological metrics like patch occupancy and home range size require less-intensive sampling, while still retaining information about contact patterns.…”

Section: Introductionmentioning

confidence: 99%

The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts

et al. 2018

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Ecologists regularly use animal contact networks to describe interactions underlying pathogen transmission, gene flow, and information transfer. However, empirical descriptions of contact often overlook some features of individual movement, and decisions about what kind of network to use in a particular setting are commonly Here, we relate individual movement trajectories to contact networks through a tripartite network model of individual, space, and time nodes. Most networks used in animal contact studies (e.g. individual association networks, home range overlap networks, and spatial networks) are simplifications of this tripartite model. The tripartite structure can incorporate a broad suite of alternative ecological metrics like home range sizes and patch occupancy patterns into inferences about contact network metrics such as modularity and degree distribution. We demonstrate the model's utility with two simulation studies using alternative forms of ecological data to constrain the tripartite network's structure and inform expectations about the harder-to-measure metrics related to contact.

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“…For convenience, many studies change the original temporal resolution of the networks under analysis (LINHARES et al, 2017b;ZHAO et al, 2018;LINHARES et al, 2019a;HOLME, 2017). In (LINHARES et al, 2019a), for instance, the aforementioned high-school network was analyzed considering each time of the MSV layout as a three-minute interval whilst the original network temporal resolution is a 20second interval per timestamp (MASTRANDREA; FOURNET; BARRAT, 2015).…”

Section: Temporal Resolutionmentioning

confidence: 99%

“…Visual clutter refers to excessive items or information close together due to edge and node overlap (ELLIS;DIX, 2007). To reduce clutter in such layouts, some efforts focus on summarizing the network by changing its original temporal resolution (i.e., by grouping edges from subsequent timestamps - Figure 1(a)) (LIN-HARES et al, 2017b;ZHAO et al, 2018;LINHARES et al, 2019a;HOLME, 2017) or by selecting relevant edges for analysis (Figure 1(b)) (ZHAO et al, 2018;HOLME, 2017;ZHAO et al, 2019). Other strategies, on the other hand, focus on changing node positioning instead of summarizing the network.…”

Section: Introductionmentioning

confidence: 99%

Improving visual analysis of streaming networks

Ponciano¹

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Temporal networks (also known as dynamic networks) are often used to model connections that occur over time between parts of a system by using nodes and edges. In temporal networks, all nodes, edges, and times, are known and available to be used in the analysis. However, in several real-world applications, data are produced in a massive and continuous way, which is known as data stream. In this case, the volume of data may be so large that the storage may be impossible and mining tasks become more challenging. In streaming temporal networks, edges are continuously arriving in non-stationary distribution. In both temporal and streaming temporal networks, patterns related to node and edge activity are typically irregular in time, which makes the visualization of such networks helpful to gain insights about network structure and dynamics. Nevertheless, the non-stationary distribution of incoming data increases complexity and turns the streaming temporal network visualization even more challenging. Several visualization layouts have been proposed, but they all have limitations. The main challenge in this context is the amount of visual information, that increases depending on the network size and density, and causes visual clutter due to edge overlap, fine temporal resolution, and node proximity. In this thesis, we propose methods to enhance the visualization of streaming temporal networks through the manipulation of the three network dimensions, namely node, edge, and time. Specifically, we propose: (i) CNO, a visual scalable node ordering method; (ii) SEVis, a streaming edge sampling method; and (iii) a streaming method that adapts the temporal resolution according to local levels of node activity. We also present a comparative study considering the combination of these methods. We show through case studies with real-world networks that each of these methods greatly improves layout readability, thus leading to a fast and reliable decision making.

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Sampling of temporal networks: Methods and biases

Cited by 32 publications

References 55 publications

Temporal Network Pattern Identification by Community Modelling

Temporal Network Pattern Identification by Community Modelling

The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts

Improving visual analysis of streaming networks

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