Search Results (4)

Graphs are an effective data structure for characterizing ubiquitous connections as well as evolving behaviors that emerge in inter-wined systems. Limited by the stereotype of node-to-node connections, learning node representations is often confined in a graph diffusion process where local information has been excessively aggregated, as the random walk of graph neural networks (GNN) explores far-reaching neighborhoods layer-by-layer. In this regard, tremendous efforts have been made to alleviate feature over-smoothing issues such that current backbones can lend themselves to be used in a deep network architecture. However, compared to designing a new GNN, less attention has been paid to underlying topology by graph re-wiring, which mitigates not only flaws of the random walk but also the over-smoothing risk incurred by reducing unnecessary diffusion in deep layers. Inspired by the notion of non-local mean techniques in the area of image processing, we propose a non-local information exchange mechanism by establishing an express connection to the distant node, instead of propagating information along the (possibly very long) original pathway node-after-node. Since the process of seeking express connections throughout a graph can be computationally expensive in real-world applications, we propose a re-wiring framework (coined the express messenger wrapper) to progressively incorporate express links in a non-local manner, which allows us to capture multi-scale features without using a very deep model; our approach is thus free of the over-smoothing challenge. We integrate our express messenger wrapper with existing GNN backbones (either using graph convolution or tokenized transformer) and achieve a new record on the Roman-empire dataset as well as in terms of SOTA performance on both homophilous and heterophilous datasets. Full article

The frequency and severity of natural disasters is surging, posing an urgent need for robust communication network infrastructure that is capable of withstanding these events. In this paper, we present a groundbreaking graph-theoretic system designed to evaluate and enhance network resilience in the face of natural disasters. Our solution harnesses the power of topological robustness metrics, integrating real-time weather data, geographic information, detailed network topology data, advanced resilience algorithms, and continuous network monitoring. The proposed scheme considers four major real-world U.S.-based network providers and evaluates their physical topologies against two major hurricanes. Our novel framework quantifies the important characteristics of network infrastructure; for instance, AT&T is identified to have fared better against Hurricane Ivan (57.98 points) than Hurricane Katrina (39.17 points). We not only provide current insights into network infrastructure resilience, but also uncover valuable findings that shed light on the performance of backbone U.S. networks during hurricanes. Furthermore, our findings provide actionable insights to enrich the overall survivability and functionality of communication networks, mitigating the adverse impacts of natural disasters on communication systems and critical services in terms of improving network resiliency via adding additional nodes and link or rewiring. Full article

A universal signature of developmental dyslexia is literacy acquisition impairments. Besides, dyslexia may be related to deficits in selective spatial attention, in the sensitivity to global visual motion, speed processing, oculomotor coordination, and integration of auditory and visual information. Whether motion-sensitive brain areas of children with dyslexia can recognize different speeds of expanded optic flow and segregate the slow-speed from high-speed contrast of motion was a main question of the study. A combined event-related EEG experiment with optic flow visual stimulation and functional frequency-based graph approach (small-world propensity ?) were applied to research the responsiveness of areas, which are sensitive to motion, and also distinguish slow/fast -motion conditions on three groups of children: controls, untrained (pre-D) and trained dyslexics (post-D) with visual intervention programs. Lower ? at ?, ?, ?1-frequencies (low-speed contrast) for controls than other groups represent that the networks rewire, expressed at ? frequencies (both speed contrasts) in the post-D, whose network was most segregated. Functional connectivity nodes have not existed in pre-D at dorsal medial temporal area MT+/V5 (middle, superior temporal gyri), left-hemispheric middle occipital gyrus/visual V2, ventral occipitotemporal (fusiform gyrus/visual V4), ventral intraparietal (supramarginal, angular gyri), derived from ?-frequency network for both conditions. After visual training, compensatory mechanisms appeared to implicate/regain these brain areas in the left hemisphere through plasticity across extended brain networks. Specifically, for high-speed contrast, the nodes were observed in pre-D (?-frequency) and post-D (?2-frequency) relative to controls in hyperactivity of the right dorsolateral prefrontal cortex, which might account for the attentional network and oculomotor control impairments in developmental dyslexia. Full article

Cancer is the second deadliest disease listed by the WHO. One of the major causes of cancer disease is tobacco and consumption possibly due to its main component, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). A plethora of studies have been conducted in the past aiming to decipher the association of NNK with other diseases. However, it is strongly linked with cancer development. Despite these studies, a clear molecular mechanism and the impact of NNK on various system-level networks is not known. In the present study, system biology tools were employed to understand the key regulatory mechanisms and the perturbations that will happen in the cellular processes due to NNK. To investigate the system level influence of the carcinogen, NNK rewired protein–protein interaction network (PPIN) was generated from 544 reported proteins drawn out from 1317 articles retrieved from PubMed. The noise was removed from PPIN by the method of modulation. Gene ontology (GO) enrichment was performed on the seed proteins extracted from various modules to find the most affected pathways by the genes/proteins. For the modulation, Molecular COmplex DEtection (MCODE) was used to generate 19 modules containing 115 seed proteins. Further, scrutiny of the targeted biomolecules was done by the graph theory and molecular docking. GO enrichment analysis revealed that mostly cell cycle regulatory proteins were affected by NNK. Full article