Analysis Of Temporal Networks Research Articles

(ω_{1},ω_{2})-temporal random hyperbolic graphs.

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities ω_{1} and ω_{2}. This extension, while conceptually simple, poses analytical challenges involving the Appell F_{1} series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ω_{1} and ω_{2} enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios.

Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle

Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared “fenceline” area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. “Fenceline” contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.

Frequent Pattern Mining in Continuous-Time Temporal Networks.

Networks are used as highly expressive tools in different disciplines. In recent years, the analysis and mining of temporal networks have attracted substantial attention. Frequent pattern mining is considered an essential task in the network science literature. In addition to the numerous applications, the investigation of frequent pattern mining in networks directly impacts other analytical approaches, such as clustering, quasi-clique and clique mining, and link prediction. In nearly all the algorithms proposed for frequent pattern mining in temporal networks, the networks are represented as sequences of static networks. Then, the inter- or intra-network patterns are mined. This type of representation imposes a computation-expressiveness trade-off to the mining problem. In this paper, we propose a novel representation that can preserve the temporal aspects of the network losslessly. Then, we introduce the concept of constrained interval graphs ( CIGs). Next, we develop a series of algorithms for mining the complete set of frequent temporal patterns in a temporal network data set. We also consider four different definitions of isomorphism for accommodating minor variations in temporal data of networks. Implementing the algorithm for three real-world data sets proves the practicality of the proposed approach and its capability to discover unknown patterns in various settings.

Link stability analysis of temporal international fertilizer trade networks

Fertilizer availability is crucial for global food security, and international fertilizer trade plays a vital role in reallocating fertilizers across multiple economies. However, the stability of international fertilizer trade relationships between economies has not been studied. Using 29 year records of the global fertilizer trade from 1990 to 2018, we construct three temporal networks linked to three primary nutrients (nitrogen N, phosphorus P2O5, and potash K2O). After introducing the link stability indicator to the international fertilizer trade system, which shows a very strong positive correlation with whether the trade will continue in the future, we analyze the factors influencing the stability of the fertilizer trade and provide advice for trading participants to establish highly stable transactions. The supply side has a greater impact on the stability of the fertilizer trade than the demand side. For exporting economies, stable exports need to focus on the counterparty’s real demand rather than its economic situation. For importing economies, intermediaries and producers with good economic conditions are stable trading partners, and trade that is geographically closer is always more stable. The methodology used for link stability analysis in this work can be applied to analyzing other directed temporal networks.

A hybrid adjacency and time-based data structure for analysis of temporal networks

Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (a node-based slice) but cannot quickly retrieve all edges that occur within a given time interval (a time-based slice). We propose a hybrid data structure for storing temporal networks that stores edges in both an adjacency dictionary, enabling rapid node-based slices, and an interval tree, enabling rapid time-based slices. Our hybrid structure also enables compound slices, where one needs to slice both over nodes and time, either by slicing first over nodes or slicing first over time. We further propose an approach for predictive compound slicing, which attempts to predict whether a node-based or time-based compound slice is more efficient. We evaluate our hybrid data structure on many real temporal network data sets and find that they achieve much faster slice times than existing data structures with only a modest increase in creation time and memory usage.

Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks

Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks.

An online and nonuniform timeslicing method for network visualisation

Visual analysis of temporal networks comprises an effective way to understand the network dynamics. It facilitates the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method that enhances temporal and streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.

Motif discovery algorithms in static and temporal networks: A survey

AbstractMotifs are the fundamental components of complex systems. The topological structure of networks representing complex systems and the frequency and distribution of motifs in these networks are intertwined. The complexities associated with graph and subgraph isomorphism problems, as the core of frequent subgraph mining, directly impact the performance of motif discovery algorithms. Researchers have adopted different strategies for candidate generation and enumeration and frequency computation to cope with these complexities. Besides, in the past few years, there has been an increasing interest in the analysis and mining of temporal networks. In contrast to their static counterparts, these networks change over time in the form of insertion, deletion or substitution of edges or vertices or their attributes. In this article, we provide a survey of motif discovery algorithms proposed in the literature for mining static and temporal networks and review the corresponding algorithms based on their adopted strategies for candidate generation and frequency computation. As we witness the generation of a large amount of network data in social media platforms, bioinformatics applications and communication and transportation networks and the advance in distributed computing and big data technology, we also conduct a survey on the algorithms proposed to resolve the CPU-bound and I/O bound problems in mining static and temporal networks.

Genetic Algorithms for Finding Episodes in Temporal Networks

The evolution of networks is a fundamental topic in network analysis and mining. One of the approaches that has been recently considered in this field is the analysis of temporal networks, where relations between elements can change over time. A relevant problem in the analysis of temporal networks is the identification of cohesive or dense subgraphs since they are related to communities. In this contribution, we present a method based on genetic algorithms and on a greedy heuristic to identify dense subgraphs in a temporal network. We present experimental results considering both synthetic and real-networks, and we analyze the performance of the proposed method when varying the size of the population and the number of generations. The experimental results show that our heuristic generally performs better in terms of quality of the solutions than the state-of-art method for this problem. On the other hand, the state-of-art method is faster, although comparable with our method, when the size of the population and the number of generations are limited to small values.

Visual querying and analysis of temporal fiscal networks

We present TeFNet, a new system for the visual analysis of temporal networks in the fiscal domain, aimed to contrast tax evasion, fiscal frauds, and money laundering. The design of TeFNet has been driven by domain experts (tax officers) and the system is currently used by the Italian revenue agency, Agenzia delle Entrate. TeFNet is based on a powerful visual query language that allows users to easily define suspicious patterns involving temporal constraints. To efficiently execute graph pattern matching algorithms on large networks, TeFNet exploits modern graph database technologies. Besides its visual query language, TeFNet is equipped with graph visualization techniques to quickly convey time-varying information during an interactive exploration of the subgraphs that match a desired pattern. Both the query language and the visualization techniques rely on a suitable timeline approach, which maps the time dimension to the space dimension. The system has been tested in a real working environment. We demonstrate its effectiveness by discussing use cases on real suspicious patterns and the results of experiments conducted with expert tax officers on real data. Furthermore, we performed a qualitative evaluation to get additional insights about advantages and limits of our system.

TM-Miner: TFS-Based Algorithm for Mining Temporal Motifs in Large Temporal Network

Temporal network is a basic tool for representing complex systems, such as communication networks and social networks; besides the temporal motif (TM) plays an important role in the analysis of temporal networks. Without considering the temporal information, most existing motif mining methods focus on static networks and are not suitable for mining temporal motifs. In this paper, we study the problem of temporal motif mining for the temporal network. To formulate the problem, we define the temporal motif as a frequently connected subgraph that has a similar sequence of information flows. Moreover, an efficient algorithm called TM-Miner is proposed. Based on the time first search (TFS) algorithm, the TM-Miner builds a canonical labeling system that uses a new lexicographic order and maps the temporal graph to the unique minimum TFS code. By utilizing the canonical labeling system, the computational cost of temporal graph isomorphism is reduced and the efficiency of the algorithm is improved. Finally, we evaluate the performance of the TM-Miner algorithm in real datasets and extensive experiments demonstrate that it is faster than the existing algorithms.

An algebraic approach to temporal network analysis based on temporal quantities

In a temporal network, the presence and activity of nodes and links can change through time. To describe temporal networks we introduce the notion of temporal quantities. We define the addition and multiplication of temporal quantities in a way that can be used for the definition of addition and multiplication of temporal networks. The corresponding algebraic structures are semirings. The usual approach to (data) analysis of temporal networks is to transform it into a sequence of time slices -- static networks corresponding to selected time intervals and analyze each of them using standard methods to produce a sequence of results. The approach proposed in this paper enables us to compute these results directly. We developed fast algorithms for the proposed operations. They are available as an open source Python library TQ (Temporal Quantities) and a program Ianus. The proposed approach enables us to treat as temporal quantities also other network characteristics such as degrees, connectivity components, centrality measures, Pathfinder skeleton, etc. To illustrate the developed tools we present some results from the analysis of Franzosi's violence network and Corman's Reuters terror news network.

Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities

Recent research on temporal networks has highlighted the limitations of a static network perspective for our understanding of complex systems with dynamic topologies. In particular, recent works have shown that i) the specific order in which links occur in real-world temporal networks affects causality structures and thus the evolution of dynamical processes, and ii) higher-order aggregate representations of temporal networks can be used to analytically study the effect of these order correlations on dynamical processes. In this article we analyze the effect of order correlations on path-based centrality measures in real-world temporal networks. Analyzing temporal equivalents of betweenness, closeness and reach centrality in six empirical temporal networks, we first show that an analysis of the commonly used static, time-aggregated representation can give misleading results about the actual importance of nodes. We further study higher-order time-aggregated networks, a recently proposed generalization of the commonly applied static, time-aggregated representation of temporal networks. Here, we particularly define path-based centrality measures based on second-order aggregate networks, empirically validating that node centralities calculated in this way better capture the true temporal centralities of nodes than node centralities calculated based on the commonly used static (first-order) representation. Apart from providing a simple and practical method for the approximation of path-based centralities in temporal networks, our results highlight interesting perspectives for the use of higher-order aggregate networks in the analysis of time-stamped network data.

Toolbox for Visual Explorative Analysis of Complex Temporal Multiscale Contact Networks Dynamics in Healthcare

Public healthcare can be cast as a complex systems and network analysis is one of the methodological approaches that are commonly used to study these types of systems. In this paper we describe a multiscale and multi-level interpretation of complex networks in public healthcare. Our contribution is to provide a toolbox for visualization and visual data-driven analysis of complex multiscale temporal contact networks that allows to simulate various dynamic processes using user-defined models. An example of explorative analysis of a dataset from real clinical data obtained from the Federal Almazov North-West Medical Research Centre in Saint Petersburg is described.

Time series analysis of temporal networks

An important feature of all real-world networks is that the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. We mainly focus on the temporal network of human face- to-face contacts and observe that it represents a stochastic process with memory that can be modeled as ARIMA. We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level <= 20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks.