Improved gravity model for identifying the influential nodes

Identifying the influential nodes in network is essential for network dynamic analysis. In this letter, inspired by the gravity model, we present an improved gravity model (EDGM) to identify the influential nodes in network through the effective distance. Firstly, we calculate the degree of nodes. Then we construct the effective distance combined with the interaction frequency between nodes, so as to establish the effective distance gravity model. Comparing with the susceptible-infected model, the results show that the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 2.36\% for the gravity model. Compared with other methods, the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 11.55%, 17.29%, 7.17% and 10.00% for the degree centrality, betweenness centrality, eigenvector centrality, and PageRank respectively. The results show that the improved gravity model could effectively identify the influential nodes in network.

Calculation of centralities in protein kinase A

Topological analysis of amino acid networks is a common method that can help to understand the roles of individual residues. The most popular approach for network construction is to create a connection between residues if they interact. These interactions are usually weighted by absolute values of correlation coefficients or mutual information. Here we argue that connections in such networks have to reflect levels of cohesion within the protein instead of a simple fact of interaction between residues. If this is correct, an indiscriminate combination of correlation and anti-correlation, as well as the all-inclusive nature of the mutual information metrics, should be detrimental for the analysis. To test our hypothesis, we studied amino acid networks of the protein kinase A created by Local Spatial Pattern alignment, a method that can detect conserved patterns formed by Cα-Cβ vectors. Our results showed that, in comparison with the traditional methods, this approach is more efficient in detecting functionally important residues. Out of four studied centrality metrics, Closeness centrality was the least efficient measure of residue importance. Eigenvector centrality proved to be ineffective as the spectral gap values of the networks were very low due to the bilobal structure of the kinase. We recommend using joint graphs of Betweenness centrality and Degree centrality to visualize different aspects of amino acid roles.

Network Analysis and Actors #CancelOmnibusLaw on Twitter Social Media Using Social Network Analysis (SNA)

Omnibus Law is the merging of several different rules into one law. RUU Cipta Kerja is one part of the Omnibus Law that attracts attention because it is considered detrimental to society. This caused a lot of rejection and protests from the society. The protest was held directly in the form of demonstrations in various regions of Indonesia and also in Twitter through #BatalkanOmnibusLaw. The purpose of this research is to find out the analysis of communication networks and identify influential actors in #BatalkanOmnibusLaw on Twitter. This research uses Social Network Analysis (SNA) methods and Computer-mediated Communication theory. Data is collected through Twitter from August 1-October 31, 2020. The process of analyzing and retrieving data is using Netlytic.org and Gephi software. The results showed that there were 62 actors with 153 interactions. Proximity between actors is worth 3, meaning close proximity and easy interaction between actors. The interactions created between actors are very few, uneven ,and the interactions that occur only one way. The #BatalkanOmnibusLaw is centered on ten actors, the most dominant account is @fraksirakyatid. Based on degree centrality analysis, closeness centrality, betweenness centrality, and eigenvector centrality the most influential actors in #BatalkanOmnibusLaw network are @fraksirakyatid and @walhinasional. Keywords: #BatalkanOmnibusLaw, Twitter, Actor, Communication Network

Exploration of the Mechanism Underlying the Association of Incident Microinfarct and Motor Deficit: A Preliminary Functional MRI Study

Background: Cerebral microinfarcts (CMIs) might cause measurable disruption to brain connections and are associated with cognitive decline, but the association between CMIs and motor impairment is still unclear. Objective: To assess the CMIs effect on motor function in vivo and explore the potential neuropathological mechanism based on graph-based network method. Methods: We identified 133 non-demented middle-aged and elderly participants who underwent MRI scanning, cognitive, and motor assessment. The short physical performance battery (SPPB) assessed motor function, including balance, walking speed, and chair stand. We grouped participants into 34 incident CMIs carriers and 99 non-CMIs carriers as controls, depending on diffusion-weighted imaging. Then we assessed the independent CMIs effects on motor function and explored neural mechanisms of CMIs on motor impairment via mapping of degree centrality (DC) and eigenvector centrality (EC). Results: CMIs carriers had worse motor function than non-carriers. Linear regression analyses showed that CMIs independently contributed to motor function. CMIs carriers had decreased EC in the precuneus, while increased DC and EC in the middle temporal gyrus and increased DC in the inferior frontal gyrus compared to controls (p < 0.05, corrected). Correlation analyses showed that EC of precuneus was related to SPPB (r = 0.25) and balance (r = 0.27); however, DC (r = –0.25) and EC (r = –0.25) of middle temporal gyrus was related with SPPB in all participants (p < 0.05, corrected). Conclusion: CMIs represent an independent risk factor for motor dysfunction. The relationship between CMIs and motor function may be attributed to suppression of functional hub region and compensatory activation of motor-related regions.

Network science inspires novel tree shape statistics

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree’s shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.

Motor imagery practice and feedback effects on functional connectivity

– Objective: the use of motor imagery (MI) in motor rehabilitation protocols has been increasingly investigated as a potential technique for enhancing traditional treatments, yielding better clinical outcomes. However, since MI performance can be challenging, practice is usually required. This demands appropriate training, actively engaging the MI-related brain areas, consequently enabling the user to properly benefit from it. The role of feedback is central for MI practice. Yet, assessing which underlying neural changes are feedback-specific or purely due to MI practice is still a challenging effort, mainly due to the difficulty in isolating their contributions. In this work, we aimed to assess functional connectivity (FC) changes following MI practice that are either extrinsic or specific to feedback. Approach: to achieve this, we investigated FC, using graph theory, in electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, during MI performance and at resting-state (rs), respectively. Thirty healthy subjects were divided into three groups, receiving no feedback (control), “false” feedback (sham) or actual neurofeedback (active). Participants underwent 12 to 13 hands-MI EEG sessions and pre- and post-MI training fMRI exams. Main results: following MI practice, control participants presented significant increases in degree and in eigenvector centrality for occipital nodes at rs-fMRI scans, whereas sham-feedback produced similar effects, but to a lesser extent. Therefore, MI practice, by itself, seems to stimulate visual information processing mechanisms that become apparent during basal brain activity. Additionally, only the active group displayed decreases in inter-subject FC patterns, both during MI performance and at rs-fMRI. Significance: hence, actual neurofeedback impacted FC by disrupting common inter-subject patterns, suggesting that subject-specific neural plasticity mechanisms become important. Future studies should consider this when designing experimental NFBT protocols and analyses.

Eigenvector centrality defines hierarchy and predicts graduation in therapeutic community units

Introduction Therapeutic communities (TCs) are mutual aid based residential programs for the treatment of substance abuse and criminal behavior. While it is expected that residents will provide feedback to peers, there has been no social network study of the hierarchy through which feedback flows. Methods Data for this study was drawn from clinical records of peer corrections exchanged between TC residents in six units kept over periods of less than two to over eight years. Four of the units served men while two served women. Hierarchy position was measured using eigenvector centrality, on the assumption that residents who were more central in the network of corrections were lower in the hierarchy. It was hypothesized that residents would rise in the hierarchy over time. This was tested using Wilcoxon paired samples tests comparing the mean and maximum eigenvector centrality for time in treatment with those in the last month of treatment. It was also hypothesized that residents who rose higher in the hierarchy were more likely to graduate, the outcome of primary interest. Logistic regression was used to test hierarchy position as a predictor of graduation, controlling for age, race, risk of recidivism as measured by the Level of Services Inventory-Revised (LSI-R) and days spent in the program. Results Residents averaged a statistically significantly lower eigenvector centrality in the last month in all units, indicating a rise in the hierarchy over time. Residents with lower maximum and average eigenvector centrality both over the length of treatment and in the last month of treatment were more likely to graduate in four of the six units, those with lower maximum and average eigenvector centrality in the last month but not over the length of treatment were more likely to graduate in one of the six units, while eigenvector centrality did not predict graduation in one unit. However, this last unit was much smaller than the others, which may have influenced the results. Conclusion These results suggest that TC residents move through a social network hierarchy and that movement through the hierarchy predicts successful graduation.

Using marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi

Anopheles stephensi is an efficient malaria vector commonly found in South Asia and the Arabian Peninsula, but in recent years it has established as an invasive species in the Horn of Africa (HoA). In this region An. stephensi was first detected in a livestock quarantine station near a major seaport in Djibouti in 2012, in Ethiopia in 2016, in Sudan in 2018 and Somalia in 2019. Anopheles stephensi often uses artificial containers as larval habitats, which may facilitate introduction through maritime trade as has been seen with other invasive container breeding mosquitoes. If An. stephensi is being introduced through maritime traffic, prioritization exercises are needed to identify locations at greatest risk of An. stephensi introduction for early detection and rapid response, limiting further invasion opportunities. Here, we use UNCTAD maritime trade data to 1) identify coastal African countries which were most highly connected to select An. stephensi endemic countries in 2011, prior to initial detection in Africa, 2) develop a ranked prioritization list of countries based on likelihood of An. stephensi introduction for 2016 and 2020 based on maritime trade alone and maritime trade and habitat suitability, and 3) use network analysis to describe intracontinental maritime trade and eigenvector centrality to determine likely paths of further introduction on the continent if An. stephensi is detected in a new location. Our results show that in 2011, Sudan and Djibouti were ranked as the top two countries with likelihood of An. stephensi introduction based on maritime trade alone, and these were indeed the first two coastal countries in the HoA where An. stephensi was detected. Trade data from 2020 with Djibouti and Sudan included as source populations identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top five countries with likelihood of An. stephensi introduction. When factoring in habitat suitability, Egypt, Kenya, Tanzania, Morocco, and Libya are ranked highest. Network analysis revealed that the countries with the highest eigenvector centrality scores, and therefore highest degrees of connectivity with other coastal African nations were South Africa (0.175), Mauritius (0.159), Ghana (0.159), Togo (0.157), and Morocco (0.044) and therefore detection of An. stephensi in any one of these locations has a higher potential to cascade further across the continent via maritime trade than those with lower eigenvector centrality scores. Taken together, these data could serve as tools to prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of countries at greatest risk of introduction may serve as an early warning system for the detection of An. stephensi, providing opportunities to limit further introduction and expansion of this invasive malaria vector in Africa.

Social Network Analysis: #BlackLivesMatter Distribution at Actor Level and System Level

#BlackLivesMatter accompanies several cases of discrimination against the black community. The hashtag was spread by actors who have great influences on Twitter users. The actors create communication network which connected to each other to form opinions about the Black Lives Matter movement. Researchers conducted a study to determine the distribution of #BlackLivesMatter at the actor level for the period 20-27 April 2021 in Twitter. The study used quantitative methods and a positivistic paradigm with a Social Network Analysis (SNA) approach. The results show that the actor with the highest degree of centrality is @jeanmessiha with 238 interactions, the actor with the highest betweenness centrality is @helloagain0611 with a value of 0.000049, the actor with the highest eigenvector centrality is @jeanmessiha with a value of 1 and there are 1,416 actors who have closeness centrality. # BlackLivesMatter has a low diameter value so that it spreads quickly but not too widely, not much reciprocity occurs, not concentrated in one dominant cluster but spread widely in several clusters. The actors play a role in spreading diverse opinions regarding Black Lives Matter, thus creating free discussion in several clusters on Twitter. Opinion widely spread on Twitter creates public opinion regarding the Black Lives Matter movement.