Bulletin of the American Physical Society
Mid-Atlantic Section Fall Meeting 2020
Volume 65, Number 20
Friday–Sunday, December 4–6, 2020; Virtual
Session F01: Physics of Complex Networks |
Hide Abstracts |
Chair: Cristiano Dias, NJIT |
Saturday, December 5, 2020 2:00PM - 2:36PM |
F01.00001: Pulse propagation in compliant complex networks. Invited Speaker: Eleni Katifori The primary role of vascular networks is to facilitate the efficient flow of nutrients where needed in the organism. A power source is necessary to overcome energy losses due to the viscosity of the fluid and maintain the flow. In animals, this is achieved via the periodic pumping of the heart, and the peristaltic pressure waves of the vessels. The pressure pulses created this way interact with the compliant, elastic vessels and propagate through the network, creating rich dynamics. We use an electrical circuit analogue to model the elastic vessels as transmission lines, and discover a robust scaling law between energy dissipation and speed of mechanical information transmission, via pressure pulse propagation. Pressure pulse propagation also plays an important role in the function of the venous and lymphatic system, which have to transmit flow against pressure gradients. Here, we discuss how traveling pulses in conjunction with properly placed valves overcome these gradients to achieve flow, and demonstrate how the coupling of the valve to the elastic vessel where it is embedded affects performance. These phenomena highlight the importance of the interactions between the fluid flow and the soft matter that surrounds it, in achieving biological function and optimizing fitness. [Preview Abstract] |
Saturday, December 5, 2020 2:36PM - 3:12PM |
F01.00002: Bayesian Exploration of Complex Networks by Random Walks Invited Speaker: Alexandre Morozov Large-scale networks represent a broad spectrum of systems in nature, science, and technology. In addition to the computer-based networks such as the World Wide Web and the Internet, online social networks such as Twitter and Facebook, and knowledge-sharing online platforms such as Wikipedia and YouTube, exert considerable influence on our everyday activities. Many of these networks are large and constantly evolving, which makes investigation of their statistical properties a challenging task. In particular, estimating the size of the network becomes non-trivial if the network is too large to visit every node. I will describe a novel methodology, based on random walks, for the inference of statistical properties of complex networks with weighted or unweighted edges. The statistics of interest include, but are not limited to, the node degree distribution, the average degree of nearest-neighbor nodes, and the node clustering coefficient. I will show how our formalism can yield high-accuracy estimates of these statistics, and of the network size, after only a small fraction of network nodes has been explored. I will demonstrate our computational framework on several standard examples, including random, scale-free, and small-world networks. I will then discuss how our methods can be used to explore Wikipedia, study propagation of infectious diseases on contact networks, and obtain population data from small samples. \\ {\bf References} 1. Kion-Crosby, W.B. and Morozov, A.V., Phys. Rev. Lett. {\bf 2018} {\it 121}, 038301 [Preview Abstract] |
Saturday, December 5, 2020 3:12PM - 3:24PM |
F01.00003: Exploring Network Communities with Random Walks Aditya Ballal, Willow Kion-Crosby, Alexandre Morozov Communities within a network are sets of nodes such that the nodes within each set are connected more densely internally than with nodes outside the set. Community structures are very common in real-world networks such as social or biological networks. Detecting community structures is equivalent to clustering which is of interest in many areas of science. We propose a computationally efficient method, based on random walks, for community detection and clustering on undirected networks with weighted or unweighted edges. The method employs first-passage properties of random walks on networks, providing key statistics of network community structure such as the number of communities and the size of each community. Our method provides a complete hierarchy of clusters which is determined by the strengths of connections between them. Surprisingly, some of the key statistics can be obtained after exploring only a small fraction of nodes which is relevant to very large real-world networks. We have used this method to cluster biological networks such as gene co-expression networks. [Preview Abstract] |
Saturday, December 5, 2020 3:24PM - 4:00PM |
F01.00004: Computational Study of the Role of Spatial Organization in the Synchronicity of Firing Events in Spiking Neuronal Networks Invited Speaker: Luis Cruz Cruz In the brain, neurons are arranged in networks at the microscopic scale whose connectivity and spatial arragements are far from random. An important theoretical and biological question is whether these kinds of organization are side effects of how the brain develops or an important component of brain functionality. As a way to tackle this question, here we asses computationally whether spatial organization of clusters of neurons in networks of spiking neurons offer advantages to the overall network firing synchronization. For this, we vary neuronal cluster connectivity for two different spatial distributions of clusters: one where clusters are arranged in biologically-inspired columns and the other where neurons from different clusters are spatially intermixed. We characterize each case by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of intercluster connectivity. We find that in both cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent only on intrinsic parameters of the biophysical model. More importantly, we find that for a specific number of connections per neuron and intercluster connectivity, the two spatial distributions of clusters differ in their response where the clusters arranged in columns have a higher degree of synchrony than the clusters that are intermixed. Implications to anatomy and brain function will be discussed. [Preview Abstract] |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700