SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

Both healthy and pathological aging exhibits gradual deterioration of structure but interestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically long-range anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

Wind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysia

This study aimed to create a Malaysian wind map of greater accuracy. Compared to a previous wind map, spatial modeling input was increased. The Genetic Algorithm-optimized Artificial Neural Network Measure–Correlate–Predict method was used to impute missing data, and managed to control over- or under-prediction issues. The established wind map was made more reliable by including surface roughness to simulate wind flow over complex terrain. Validation revealed that the current wind map is 33.833% more accurate than the previous wind map. Furthermore, the correlation coefficient between wind map-simulated data and observed data was high as 0.835. In conclusion, the new and improved wind map for Malaysia simulates data with acceptable accuracy.

What is bridge centrality? A comment on Jones, Ma, and McNally (2019)

Jones, Ma, and McNally (2019) introduce a novel network measure called bridge centrality, which aims to quantify psychopathological symptoms that may contribute to comorbidity. Centrality measures in psychometric networks have come under scrutiny recently with several researchers arguing for more explicit interpretations of what they purport to measure. One useful strategy has been to connect centrality measures to more traditional psychometric measures such as factor loadings. In this commentary, we aim to do the same by connecting bridge centrality to a recently developed measure called network loadings, which are roughly equivalent with factor loadings. By re-analyzing a recent simulation study, we demonstrate that bridge centrality (specifically bridge strength and bridge expected influence) are nothing more than the (absolute) sum of network cross-loadings. We provide an empirical example to demonstrate this equivalency as well as to show some potential shortcomings of bridge centrality when there are more than two disorders in a network. We raise concerns over bridge centrality’s utility and recommend researchers use network loadings instead.

A climate network perspective of the intertropical convergence zone

The intertropical convergence zone (ITCZ) is an important component of the tropical rain belt. Climate models continue to struggle to adequately represent the ITCZ and differ substantially in its simulated response to climate change. Here we employ complex network approaches, which extract spatio-temporal variability patterns from climate data, to better understand differences in the dynamics of the ITCZ in state-of-the-art global circulation models (GCMs). For this purpose, we study simulations with 14 GCMs in an idealized slab-ocean aquaplanet setup from TRACMIP – the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project. We construct network representations based on the spatial correlation pattern of monthly surface temperature anomalies and study the zonal mean patterns of different topological and spatial network characteristics. Specifically, we cluster the GCMs by means of their zonal network measure distribution utilizing hierarchical clustering. We find that in the control simulation, the zonal network measure distribution is able to pick up model differences in the tropical SST contrast, the ITCZ position and the strength of the Southern Hemisphere Hadley cell. Although we do not find evidence for consistent modifications in the network structure tracing the response of the ITCZ to global warming in the considered model ensemble, our analysis demonstrates that coherent variations of the global SST field are linked with ITCZ dynamics. This suggests that climate networks can provide a new perspective on ITCZ dynamics and model differences therein.

Geometrical congruence and efficient greedy navigability of complex networks

Hyperbolic networks are supposed to be congruent with their underlying latent geometry and following geodesics in the hyperbolic space is believed equivalent to navigate through topological shortest paths (TSP). This assumption of geometrical congruence is considered the reason for nearly maximally efficient greedy navigation of hyperbolic networks. Here, we propose a complex network measure termed geometrical congruence (GC) and we show that there might exist different TSP, whose projections (pTSP) in the hyperbolic space largely diverge, and significantly differ from the respective geodesics. We discover that, contrary to current belief, hyperbolic networks do not demonstrate in general geometrical congruence and efficient navigability which, in networks generated with nPSO model, seem to emerge only for power-law exponent close to 2. We conclude by showing that GC measure can impact also real networks analysis, indeed it significantly changes in structural brain connectomes grouped by gender or age.

On the Equivalency of Factor and Network Loadings

Recent research has demonstrated that the network measure node strength or sum of a node’s connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).