Time series analysis of temporal networks

Many real-world systems can be expressed in temporal networks with nodes playing different roles in structure and function, and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinions or epidemics, predict leading figures in academia, conduct advertisements for various commodities and so on. However, it is rather difficult to identify critical nodes, because the network structure changes over time in temporal networks. In this paper, considering the sequence topological information of temporal networks, a novel and effective learning framework based on the combination of special graph convolutional and long short-term memory network (LSTM) is proposed to identify nodes with the best spreading ability. The special graph convolutional network can embed nodes in each sequential weighted snapshot and LSTM is used to predict the future importance of timing-embedded features. The effectiveness of the approach is evaluated by a weighted Susceptible-Infected-Recovered model. Experimental results on four real-world temporal networks demonstrate that the proposed method outperforms both traditional and deep learning benchmark methods in terms of the Kendall τ coefficient and top k hit rate.

One of the most characteristic features of biological molecular networks is that the network structure itself changes, depending on the cellular environment. Indeed, activated molecules show a variety of responses to distinctive cell conditions, and subsequently the network structures of active molecules also change. Here we present an approach to trace the network structure changes by using the graphical chain model developed from the gene expression data. The previous procedure for applying the graphical chain model to the expression profiles of a limited number of genes has been improved to analyze the entire set of genes. Furthermore, the chain model has been rearranged according to the association strength, and was scrutinized to identify the candidates of essential gene-gene relationships for the network changes, by using the path consistency algorithm. The improved procedure was applied to the expression profiles of 8,427 genes, which were measured in two distinctive stages of liver cancer progression. As a result, the chain model of the 18 gene cluster relationships with strong associations was inferred, in which the coordination of clusters was described in the cell stage progression, and the gene-gene relationships between known cancer-related genes causing the progression were further refined. Thus, the present procedure is a useful method to model the network structure changes in the cell stage progression, and to clarify the gene candidates for the progression.

Using network ecology to understand and mitigate long‐term insect declines

The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.

Network analysis provides a powerful framework for the interpretation of genome-wide data. While static network approaches have proved fruitful, there is increasing interest in the insights gained from the analysis of cellular networks under different conditions. In this work, we study the effect of stress on cellular networks in fission yeast. Stress elicits a sophisticated and large scale cellular response, involving a shift of resources from cell growth and metabolism towards protection and maintenance. Previous work has suggested that these changes can be appreciated at the network level. In this paper, we study two types of cellular networks: gene co-regulation networks and weighted protein interaction networks. We show that in response to oxidative stress, the co-regulation networks re-organize towards a more modularised structure: while sets of genes become more tightly co-regulated, co-regulation between these modules is decreased. This shift translates into longer average shortest path length, increased transitivity, and decreased modular overlap in these networks. We also find a similar change in structure in the weighted protein interaction network in response to both oxidative stress and nitrogen starvation, confirming and extending previous findings. These changes in network structure could represent an increase in network robustness and/or the emergence of more specialised functional modules. Additionally, we find stress induces tighter co-regulation of non-coding RNAs, decreased functional importance of splicing factors, as well as changes in the centrality of genes involved in chromatin organization, cytoskeleton organization, cell division, and protein turnover.

The impact of interorganizational systems (IOSs) on the structure of market networks is analyzed from a management perspective. A research framework is applied to various organizational settings, yielding a range of mixed mode forms in which elements of both market and hierarchy are evident. These forms are more complex than the simple network or hybrid structures postulated in the management and information systems literature. The framework represents a departure from electronic markets theory, questioning its basic predictions that as companies trade electronically there will be proportionately more markets than hierarchies. Instead, IOSs make possible relationships that combine market and hierarchy elements simultaneously. Although economic forces are driving the changes in network structure, economic variables are tempered by individual firm strategies reflecting investment, network structure and IOS choices. The implications for theory are that traditional analyses of network structures and competition in business markets do not describe or explain adequately the structure and dynamics of competition in an electronic trading environment. The implications for managers are that they should consider the effects of mixed mode network structures on their processes for forming and managing business relationships supported by IOSs. The contribution of the paper is to provide a more accurate model of competition in business markets by demonstrating that multiple forms of mixed mode network structures exist. The mixed mode proposition is illustrated with case data from a range of mixed mode network structures.

While quantitative ethnographers have used epistemic network analysis (ENA) to model trajectories that show change in network structure over time, visualizing trajectory models in a way that facilitates accurate interpretation has been a significant challenge. As a result, ENA has predominantly been used to construct aggregate models, which can obscure key differences in how network structures change over time. This study reports on the development and testing of a new approach to visualizing ENA trajectories. It documents the challenges associated with visualizing ENA trajectory models, the features constructed to address those challenges, and the design decisions that aid in the interpretation of trajectory models. To test this approach, we compare ENA trajectory models with aggregate models using a dataset with previously published results and known temporal features. This comparison focuses on interpretability and consistency with prior qualitative analysis, and we show that ENA trajectories are able to represent information unavailable in aggregate models and facilitate interpretations consistent with qualitative findings. This suggests that this approach to ENA trajectories is an effective tool for representing change in network structure over time.

Hematopoiesis is an extensively studied model system for cell differentiation. Cell-type-specific gene expression patterns are observed during hematopoiesis. Gene expression is governed by regulatory networks composed of cell-type-specific transcription factors. Resolving the transcriptional regulatory network for cell-type-specific gene expression provides a promising means of understanding the mechanisms underlying cell fate decisions. In this study, transcriptional regulatory networks in hematopoietic stem and progenitor cells were predicted based on gene expression profiles and distributions of transcription factor binding motifs in the promoter regions of cell-type-specific transcription factors. In particular, structural changes that occur when pluripotent stem cells progress to lineage-committed progenitors were evaluated. Marked changes in the regulatory circuit of transcription throughout the differentiation process could be elucidated by network analysis. Modular structures were a frequently described feature of biological networks observed in estimated networks. Within a module, most transcription factors were found to be regulated by a small number of regulators acting as downstream targets. Certain regulators within these modules coincide with known key regulators of hematopoietic cell differentiation. In addition to the modular structure, a twolayered structure was clearly observed in progenitor regulatory networks. Transcription factors could be distinctly divided into regulators within the regulatory layer and into targets in the output layer according to their degree of distribution. The restriction of mutual regulation between transcription factors was remarkable in that it allowed for alterations in network structures between hematopoietic stem cells and progenitors. Thus, using this approach, the relationships among transcription factors could be revealed by a reduction in mutual regulation to form a modular structure within the regulatory network

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Cooperation between ports is known to enhance their competitiveness. This study aims to reveal changes in the global shipping network structure and evaluate the competitiveness of cooperating ports in the shipping network as port connectivity. We apply a backbone flow algorithm, which creates an initial shipping network with backbone flow and reconstructs the network using an adaptive large-neighborhood search, to solve a liner shipping network design problem. Network science measures are used to evaluate changes in network structure and port connectivity. The cooperation between the Osaka and Kobe ports is analyzed as a case study. We find that draft development allows large-sized vessels, which cannot currently call at the Osaka and Kobe ports, to dock there, enhancing their competitiveness. This increased competitiveness surpasses that of the Kaohsiung port but remains lower than that of other large Asian hub ports, including Busan, Singapore, and Tanjung Pelepas. Our findings provide guidelines for developing port strategies to attract more port calls and enhance competitiveness through port cooperation.

Agent-based modeling of China’s rural–urban migration and social network structure

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Temporal networks come with a wide variety of heterogeneities, from burstiness of event sequences to correlations between timings of node and link activations. In this paper, we set to explore the latter by using greedy walks as probes of temporal network structure. Given a temporal network (a sequence of contacts), greedy walks proceed from node to node by always following the first available contact. Because of this, their structure is particularly sensitive to temporal-topological patterns involving repeated contacts between sets of nodes. This becomes evident in their small coverage per step as compared to a temporal reference model -- in empirical temporal networks, greedy walks often get stuck within small sets of nodes because of correlated contact patterns. While this may also happen in static networks that have pronounced community structure, the use of the temporal reference model takes the underlying static network structure out of the equation and indicates that there is a purely temporal reason for the observations. Further analysis of the structure of greedy walks indicates that burst trains, sequences of repeated contacts between node pairs, are the dominant factor. However, there are larger patterns too, as shown with non-backtracking greedy walks. We proceed further to study the entropy rates of greedy walks, and show that the sequences of visited nodes are more structured and predictable in original data as compared to temporally uncorrelated references. Taken together, these results indicate a richness of correlated temporal-topological patterns in temporal networks.

This chapter presents a survey of recent methods for reconstruction of time-varying biological networks such as gene interaction networks based on time series node observations (e.g. gene expressions) from a modeling perspective. Time series gene expression data has been extensively used for analysis of gene interaction networks, and studying the influence of regulatory relationships on different phenotypes. Traditional correlation and regression based methods have focussed on identifying a single interaction network based on time series data. However, interaction networks vary over time and in response to environmental and genetic stress during the course of the experiment. Identifying such time-varying networks promises new insight into transient interactions and their role in the biological process. A key challenge in inferring such networks is the problem of high-dimensional data i.e. the number of unknowns p is much larger than the number of observations n. We discuss the computational aspects of this problem and examine recent methods that have addressed this problem. These methods have modeled the relationship between the latent regulatory network and the observed time series data using the framework of probabilistic graphical models. A key advantage of this approach is natural interpretability of network reconstruction results; and easy incorporation of domain knowledge into the model. We also discuss methods that have addressed the problem of inferring such time-varying regulatory networks by integrating multiple sources or experiments including time series data from multiple perturbed networks. Finally, we mention software tools that implement some of the methods discussed in this chapter. With next generation sequencing promising yet further growth in publicly available -omics data, the potential of such methods is significant.

Rheological analysis as a means for determining the silane crosslink network structure and content in crosslinked polymer composites