scholarly journals Time-varying nodal measures with temporal community structure: a cautionary note to avoid misinterpretation

2019 ◽  
Author(s):  
WH Thompson ◽  
G Kastrati ◽  
K Finc ◽  
J Wright ◽  
JM Shine ◽  
...  
Keyword(s):  
Community Structure ◽  
Network Models ◽  
Time Varying ◽  
Temporal Network ◽  
Cautionary Note ◽  
Network Properties ◽  
Nodal Properties ◽  
Network Neuroscience

AbstractIn network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behaviour. Here we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occuring. Further, we present a temporal extension to the participation coefficient measure (temporal participation coefficient) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node’s integration through time while adjusting for the possible changes in the community structure of the overall network.

scholarly journals Time‐varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation

2020 ◽  
Vol 41 (9) ◽  
pp. 2347-2356 ◽  
Author(s):  
William Hedley Thompson ◽  
Granit Kastrati ◽  
Karolina Finc ◽  
Jessey Wright ◽  
James M. Shine ◽  
...  
Keyword(s):  
Community Structure ◽  
Time Varying ◽  
Cautionary Note

Geo-Temporal, Network Properties of the Chinese COVID-19 Epidemic

2020 ◽  
Author(s):  
Ariel L Rivas ◽  
Jose Febles Patron ◽  
Steve D. Smith ◽  
Folorunso Fasina ◽  
James B. Hittner
Keyword(s):  
Temporal Network ◽  
Network Properties

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Network Embedding on Hierarchical Community Structure Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Network Properties ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

scholarly journals Multi-model inference of network properties from incomplete data

2006 ◽  
Vol 3 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Michael P. H. Stumpf ◽  
Thomas Thorne
Keyword(s):  
Network Models ◽  
Network Data ◽  
Global Networks ◽  
New Approach ◽  
Model Inference ◽  
Novel Approach ◽  
Eukaryotic Species ◽  
Network Properties ◽  
Experimental Approaches ◽  
Saccaromyces Cerevisiae

Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

scholarly journals Changes in network centrality of psychopathology symptoms between the COVID-19 outbreak and after peak

2020 ◽  
Vol 25 (12) ◽  
pp. 3140-3149
Author(s):  
Yuanyuan Wang ◽  
Zhishan Hu ◽  
Yi Feng ◽  
Amanda Wilson ◽  
Runsen Chen
Keyword(s):  
Motor Skills ◽  
Network Models ◽  
Anxiety And Depression ◽  
Psychological Interventions ◽  
Network Centrality ◽  
Patient Health ◽  
Network Properties ◽  
Psychopathology Symptoms ◽  
Psychomotor Symptoms ◽  
The Relationship

AbstractThe current study investigated the mechanism and changes in psychopathology symptoms throughout the COVID-19 outbreak and after peak. Two studies were conducted separately in China during outbreak and the after peak stages, with 2540 participants were recruited from February 6 to 16, 2020, and 2543 participants were recruited from April 25 to May 5, 2020. The network models were created to explore the relationship between psychopathology symptoms both within and across anxiety and depression, with anxiety measured by the Generalized Anxiety Disorder-7 and depression measured by the Patient Health Questionnaire-9. Symptom network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between the outbreak and after peak. Noticeably, psychomotor symptoms such as impaired motor skills, restlessness, and inability to relax exhibited high centrality during the outbreak, which still relatively high but showed substantial remission during after peak stage (in terms of strength, betweenness, or bridge centrality). Meanwhile, symptoms of irritability (strength, betweenness, or bridge centrality) and loss of energy (bridge centrality) played an important role in the network after the peak of the pandemic. This study provides novel insights into the changes in central features during the different COVID-19 stages and highlights motor-related symptoms as bridge symptoms, which could activate the connection between anxiety and depression. The results revealed that restrictions on movement were associated with worsen in psychomotor symptoms, indicating that future psychological interventions should target motor-related symptoms as priority.

scholarly journals Effect of antipsychotics on community structure in functional brain networks

2019 ◽  
Vol 7 (6) ◽  
pp. 932-960 ◽  
Author(s):  
Ryan Flanagan ◽  
Lucas Lacasa ◽  
Emma K Towlson ◽  
Sang Hoon Lee ◽  
Mason A Porter
Keyword(s):  
Community Structure ◽  
Network Effects ◽  
Brain Networks ◽  
Relevant Information ◽  
Control Group ◽  
Functional Brain ◽  
Structure Changes ◽  
Structural Abnormalities ◽  
Functional Brain Networks ◽  
Network Properties

AbstractSchizophrenia, a mental disorder that is characterized by abnormal social behaviour and failure to distinguish one’s own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. In this article, we (1) investigate whether mesoscale network properties give relevant information to distinguish groups of patients from controls in different scenarios and (2) use this lens to examine network effects of different antipsychotic treatments. Using various methods of network analysis, we examine the effect of two classical therapeutic antipsychotics—Aripiprazole and Sulpiride—on the architecture of functional brain networks of both controls (i.e., a set of people who were deemed to be healthy) and patients (who were diagnosed with schizophrenia). We compare community structures of functional brain networks of different individuals using mesoscopic response functions, which measure how community structure changes across different scales of a network. Our approach does a reasonably good job of distinguishing patients from controls, and the distinction is sharper for patients and controls who have been treated with Aripiprazole. Unexpectedly, we find that this increased separation between patients and controls is associated with a change in the control group, as the functional brain networks of the patient group appear to be predominantly unaffected by this drug. This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia, something that conflicts with the naive assumption that the drug alters the mesoscale network properties of the patients (rather than the controls). By contrast, we are less successful at separating the networks of patients from those of controls when the subjects have been given the drug Sulpiride. Taken together, in our results, we observe differences in the effects of the drugs (and a placebo) on community structure in patients and controls and also that this effect differs across groups. From a network-science perspective, we thereby demonstrate that different types of antipsychotic drugs selectively affect mesoscale properties of brain networks, providing support that structures such as communities are meaningful functional units in the brain.

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