Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session W24: The Statistical Physics of Realworld Networks IUndergrad Friendly

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Sponsoring Units: GSNP Chair: Guido Caldarelli, IMT Alti Studi Lucca Room: 401 
Friday, March 6, 2020 8:00AM  8:12AM 
W24.00001: Scalefree networks revealed from finitesize scaling Matteo Serafino, Giulio Cimini, Amos Maritan, Samir Suweis, Jayanth R Banavar, Guido Caldarelli Network theory is a powerful tool to develop predictive models of physical, biological and social collective phenomena. A remarkable feature of many networks observed in nature is that they are approximately scale free: the fraction of nodes with k incident links (the degree) follows a power law for sufficiently large k. The value of the power law exponent as well as deviations from such scaling behavior provide invaluable information on the mechanisms underlying the formation of the network. Importantly, real networks are not infinitely large and the largest degree cannot be larger than the number of nodes. Finite size scaling is a useful tool for analyzing deviations from power law behavior in the vicinity of a critical point in a physical system arising due to a finite correlation length. Here we show that despite the essential differences between networks and critical phenomena, finite size scaling provides a powerful framework for analyzing selfsimilarity and the scale free nature of real networks. We analyze about two hundred naturally occurring networks with distinct dynamical origins, and find that a large number of these follow the finite size scaling hypothesis without any selftuning. 
Friday, March 6, 2020 8:12AM  8:24AM 
W24.00002: Statistical Physics and Twitter analysis Guido Caldarelli, Carolina Becatti, Rocco De Nicola, Fabio Del Vigna, Renaud Lambiotte, Marinella Petrocchi, Fabio SARACCO In this work we analyse approximately 10^6 tweets exchanged during the last Italian elections held on March 4, 2018. Using an entropybased null model discounting the activity of the users, we first identify potential political alliances within the group of verified accounts: if two verified users are retweeted more than expected by the nonverified ones, they are likely to be related. Then, we derive the users’ affiliation to a coalition measuring the polarisation of unverified accounts. Finally, we study the bipartite directed representation of the tweets and retweets network, in which tweets and users are collected on the two layers. Unexpectedly for most of the users, automated accounts, also known as social bots, contribute more and more to this process of news spreading Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers 
Friday, March 6, 2020 8:24AM  8:36AM 
W24.00003: Using Correlated Stochastic Differential Equations to Model Cryptocurrency Rates and Social Media Activities Stephen Dipple, Abhishek Chaudhary, James Flamino, Boleslaw Szymanski, Gyorgy Korniss Increasingly interconnected financial systems and online social networks present both critical challenges and opportunities. Volatility in the former (e.g., cryptocurrency rates) can give rise to increased volume of activities in online social networks on relevant topics, while sentiments and rumors in online social networks can also have a significant impact on the corresponding financial time series. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noisecorrelated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric OrnsteinUhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities, we estimate parameters, and using the observed correlations, forecast selected time series. 
Friday, March 6, 2020 8:36AM  8:48AM 
W24.00004: Optimizing Network Structure in a Network Model of Human Aging Garrett Stubbings, Andrew Rutenberg Our network model of aging represents aspects of human health as nodes in a complex network, these nodes damage stochastically over time based on the health of their neighbours. This represents the accumulation of damage leading to poor health and eventual mortality. Previous work has shown that by using a scale free network this model captures the phenomenology of health and mortality in human populations. Why do scale free networks best capture this phenomenology? Do these scale free networks represent an organism’s robustness to damage in a meaningful way? We address this question from the bottom up using a network optimization approach. Beginning at a random network structure, we optimize the network structure with respect to various health outcomes, such as longevity and healthrelated quality of life. We investigate which network motifs emerge depending on the optimization health outcome, and how these aspects of the network structure affect damage propagation. Knowledge of how damage propagates between different types of nodes will aid our understanding of how different aspects of human health interact, and how humans accumulate health deficits over time. 
Friday, March 6, 2020 8:48AM  9:00AM 
W24.00005: Cascading dynamic slowdowns around hacked vehicles Skanda Vivek, David B Yanni, Jesse L Silverberg, Peter Yunker Almost 40% of vehicles in the US can connect to the internet. While there are significant benefits of increased connectivity, hackers have illustrated that internet connected vehicles can be compromised remotely. Further, largescale hacks have the potential to cause disastrous citywide disruptions. Recently, it was shown using percolation theory that randomly stalling 20% of cars in Manhattan would cause a total freeze of city traffic. While this upperbound estimate served as the first quantification of cityscale disruptions when vehicles are hacked, dynamic effects potentially cause significant slowdowns at lower fractions of hacked vehicles. Here, we perform simulations of traffic dynamics around hacked vehicles on grids of onelane roads, using the SUMO traffic simulation platform. We consider slowdowns, when vehicles encounter road blocks from hacked vehicles and find that shortly posthack, blocked roads cascade to neighboring regions, leading to significant slowdowns across the grid. We find that slowdowns are a function of hacked vehicle density, time since hack, and grid size. At large enough wait times, even low fractions of hacked vehicles cause significant gridlocks. Our results provide insights for recovery and rerouting to mitigate impacts of a largescale hack. 
Friday, March 6, 2020 9:00AM  9:12AM 
W24.00006: Inference of Network Communities using Random Walks Aditya Ballal, Willow KionCrosby, Alexandre Morozov Community structures are very common in realworld networks. For example, social networks such as Facebook, Instagram and Twitter, biological networks such as gene coexpression networks, proteinprotein interaction networks or link based networks such as Wikipedia all exhibit pronounced community structure. We propose a novel stochastic method, based on random walks, for community detection on undirected networks with weighted or unweighted edges. The method employs firstpassage 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 after only a small fraction of nodes have been explored. This method provides robust results on largescale networks in which the complete transition matrix is unavailable due to network size. 
Friday, March 6, 2020 9:12AM  9:24AM 
W24.00007: Resolution limit revisited: community detection using generalized modularity density Jiahao Guo, Pramesh Singh, Kevin E. Bassler Various attempts have been made in recent years to solve the Resolution Limit (RL) problem in community detection by considering variants of the modularity metric in the detection algorithms. These metrics purportedly largely mitigate the RL problem and are preferable to modularity in many realistic scenarios. However, they are not generally suitable for analyzing weighted networks or for detecting hierarchical community structure. Resolution limit problems can be complicated, though, and in particular it can be unclear when it should be considered as problem. In this paper, we introduce a metric that we call generalized modularity density Q_{g} that eliminates the RL problem at any desired resolution scale and is easily extendable to study weighted, directed, and hierarchical networks. We also propose a benchmark test to quantify the resolution limit problem, examine various modularitylike metrics to show that the new metric Q_{g} performs best, and show that Q_{g} can identify modular structures in realworld and artificial networks that are otherwise hidden. 
Friday, March 6, 2020 9:24AM  9:36AM 
W24.00008: A novel community detection method improves detection of functional gene modules in big gene expression data. Pramesh Singh, Jiahao Guo, Priyanka Bhandary, Eve S. Wurtele, Kevin E. Bassler We identify communities of functionally related genes in the network inferred from the gene expression data of eukaryotic model organisms Arabidopsis thaliana & Saccharomyces cerevisiae by finding the network partition that maximizes the recently introduced generalized modularity density metric Q_{g}. This new metric does not suffer from the resolution limit problem and, with its tunable control parameter, can be used to study the hierarchical structure of communities. We use the Reduced Network Extremal Ensemble Learning (RenEEL) scheme [Sci. Rep. 9, 14234 (2019)] to optimize the metric. Statistical significance comparisons with the gene ontology indicate that the Q_{g} method outperforms other clustering methods. Orphan genes have been found in all sequenced species. These are genes unique to particular species. They are thought to play a key role in speciation, but their regulatory interactions remain largely unknown. Focusing on highly significant functional modules that contain orphan genes, regulatory interaction patterns involving these genes are discovered and testable predictions are made about their specific biological functions. 
Friday, March 6, 2020 9:36AM  9:48AM 
W24.00009: The broken symmetry of music: applying statistical physics to understand the structure of musical harmony Jesse Berezovsky Despite myriad musical systems and styles, certain characteristics are nearly universal across cultures and throughout history, including a restriction to a discrete set of sound frequencies (pitches). In this talk, I will present a bottomup approach to a theory of musical harmony, starting from two basic (and conflicting) principles: a system of music is most effective when it 1. minimizes dissonant sounds, and 2. permits sufficient complexity to allow the desired artistic expression. By quantifying these principles and assuming a parameter (temperature) that specifies the balance between them, the problem directly maps onto standard statistical mechanics [1]. A mean field treatment reveals phase transitions from disordered sound to ordered phases with distributions of pitches that closely match musical tuning systems used throughout the history of both western and nonwestern music. A numerical model with nearestneighbor interactions displays the behavior of an XY system, including the appearance of topological defects following a quench. These defects, arising from the KibbleZurek mechanism, are interpreted as chords, with their arrangement reflecting a system of harmony. 
Friday, March 6, 2020 9:48AM  10:00AM 
W24.00010: Evolution of coauthorship networks in view of simplicial complex Byungnam Kahng, Yongsun Lee, Deokjae Lee Graph, composed of nodes and links, is a simple representation for constituents and pairwise interactions, respectively. This simple method was successful for explaining diverse properties of complex systems to some extent. Hypergraph including simplicial complex is a generalization of graph, which takes into account of more than pairwise interactions between multiple nodes. Here, using this simplicial complex representation based on algebraic topology, we consider the evolution of coauthorship networks, a prototypical example of largescale social relationships, based on empirical datasets on specific subfields in science. We found that the facet degree distribution exhibits powerlaw decaying behavior more elaborately than the graph degree distribution, and the first Betti number is useful for representing the emergence of a largescale cooperative phenomenon. Moreover, we construct a model to reproduce such results, which would be useful for understanding further structural properties of such simplex complexes. 
Friday, March 6, 2020 10:00AM  10:12AM 
W24.00011: Impact Factor volatility to a single paper: A comprehensive analysis of 11639 journals Manolis Antonoyiannakis We study how a single paper affects the Impact Factor (IF) by analyzing data from 3,088,511 papers published in 11639 journals in the 2017 Journal Citation Reports of Clarivate Analytics. We find that IFs are highly volatile. For example, the topcited paper of 381 journals caused their IF to increase by more than 0.5 points, while for 818 journals the relative increase exceeded 25%. And one in 10 journals had their IF boosted by more than 50% by their top three cited papers. Because the singlepaper effect on the IF is inversely proportional to journal size, small journals are rewarded much more strongly than large journals for a highlycited paper, while they are penalized more for a lowcited paper, especially if their IF is high. This skewed reward mechanism incentivizes highIF journals to stay small, to remain competitive in rankings. We discuss the implications for breakthrough papers to appear in prestigious journals. We also question the reliability of IF rankings given the high IF sensitivity to a handful of papers for thousands of journals. 
Friday, March 6, 2020 10:12AM  10:24AM 
W24.00012: A mathematical analysis of Stock price oscillations within financial markets. Leonard Mushunje The application of econophysics in modeling investment assets’ market behavior is considerably increasing and is highly becoming an area of interest for econophysicists. This study investigated stock price oscillatory behavior in stock markets. We applied mathematical methods to derive the stock market price oscillatory model from the physics field. We considered two distinct price level cases that is, high and low price cases and presented/ derived a corresponding model for each case. We managed also to derive an explicit time function which measures and calculate the time taken by stock prices to oscillate between two values. Also, from the lowprice oscillation model we managed to investigate stock price motion at different times with all other external forces held constant. Results obtained showed that, although stock price movement (volatility) is time dependent, it is propelled and fueled by market forces such as stock volume, market size and classical forces of demand and supply. Above all we evaluated our model using means difference test of hypothesis using actual and estimated stock price data. We failed to reject our null hypothesis and concluded that, there is no statistical significant difference in the means which highly support the precision of our model. 
Friday, March 6, 2020 10:24AM  10:36AM 
W24.00013: When money beats time: the effect of lengthdependent costs on transport driven by longrange connections Jayson Paulose, Tom Suter, Oskar Hallatschek Finding the optimal route between pairs of spatiallyseparated points is a prevalent problem in technology (transport and communication networks) and nature (foraging and migration). In many situations, each segment of a route is associated with a stochastic waiting time determined by the frequency of making connections over a given distance (the jump rate), and a deterministic travel cost which is a growing function of the connection length (the cost function). The optimal route minimizes a weighted combination of time and cost. When deterministic costs are ignored, broad jumprate distributions which fall off slowly with distance can dramatically speed up optimal routes: nearly all the distance between origin and destination is covered in a single segment which can be found in a short time. However, introducing a deterministic cost makes these long connections prohibitively expensive, and less useful for optimal routing. We study the tradeoff between broad jump rate distributions and growing cost functions in a model that generates ensembles of optimal routes for specified jump rates and cost functions. We find that even gentlygrowing cost functions (which grow slower than linearly with distance) can strongly suppress the acceleration due to broad jumprate distributions. 
Friday, March 6, 2020 10:36AM  10:48AM 
W24.00014: Quantum mechanism of price stabilization in financial markets Jack Sarkissian We study the mechanism of price stabilization in financial instruments using the quantum coupledwave model and show that security mispricing results in persistent execution imbalance, which drives the price towards its fair value. In the course of stabilization the bidask spread widens, which is in agreement with observed market data. If initial mispricing is large, price may exhibit oscillatory behavior as the normally present random phase jumps are suppressed. This also is a widely observed empirical pattern. When the fair price is achieved, imbalance stabilizes at zero value and fluctuates around it. These fluctuations are smaller for highly liquid securities, and larger for less liquid securities, which shows that liquid securities are less tolerable to mispricing. This behaviour is again in agreement with market data. These results show that price stabilization can be viewed as a quantumchaotic process resulting from balance between buyers and sellers quoting prices on different bid and ask levels, rather than agreeing on a single price, as is widely believed in the economic supplydemand balance paradigm. 
Friday, March 6, 2020 10:48AM  11:00AM 
W24.00015: Hybrid phase transitions driven by tugofwar mechanism in complex dynamic systems Byungnam Kahng, Jinha Park, Sudo Yi, kwangjong choi Hybrid phase transitions, exhibiting a jump of the order parameter and criticality at a transition point, are observed in diverse complex systems. Examples include kcore percolation, cascading breakdown in interdependent networks, contagion dynamics, restricted cluster coagulations, synchronization, etc. For such dynamical systems, it is a challenging task to uncover underlying mechanisms, and according to which the hybrid phase transitions are classified into universality classes. Here, we perform such tasks. Particularly, we focus on the case that the socalled tugofwar mechanism between two temporal subgroups of the system induces hybrid phase transitions in dynamical systems such as percolation and synchronization. We show that in such dynamics, interevent times between two successive crossings in the tugofwar process have an interevent time distribution that exhibits powerlaw decay behavior. The associated exponent determines the criticality of the hybrid percolation transition through cluster merging dynamics. 
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