Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session A02: Network Theory and Applications to Complex SystemsFocus
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Sponsoring Units: GSNP Chair: Guido Caldarelli, University of Venice Ca'Foscari Room: Room 125 |
Monday, March 6, 2023 8:00AM - 8:36AM |
A02.00001: Collective dynamical regimes and synchronization transitions in brain networks Invited Speaker: Raffaella Burioni The cerebral cortex exhibits spontaneous activity even in the absence of any task or external stimuli. A salient aspect of this resting-state dynamics, as revealed by in vivo and in vitro measurements, is that it exhibits several patterns, including collective oscillations, emerging out of neural synchronization, as well as highly-heterogeneous outbursts of activity interspersed by periods of quiescence, called "neuronal avalanches". It has been conjectured that such a state is best described as a critical dynamical process - whose nature is still not fully understood - where scale-free avalanches of activity emerge at the edge of a phase transition. In particular, some works suggest that this is most likely a synchronization transition, separating synchronous from asynchronous phases. By investigating simplified models of coupled excitable oscillators on brain networks describing the cortex dynamics at a mesoscopic level, we discuss the possible nature of such a synchronization phase transition in structurally heterogeneous systems. |
Monday, March 6, 2023 8:36AM - 8:48AM |
A02.00002: Network Reconstruction from Noisy and Incomplete Spreading Dynamics Mateusz Wilinski, Andrey Y Lokhov Long distance connections in modern interconnected world play an important role in many areas of life, such as information spreading, epidemics, financial contagion or opinion dynamics. This drives the need for proper understanding of diffusion and spreading processes on networks. Another unprecedented feature of current era is the data availability, which, together with rapid development of machine learning tools, allow to learn and predict models from observed processes. In reality, however, these large amounts of data are often incomplete, noisy or biased. We address this problem in the case of spreading processes on networks and propose a general framework, which allow to learn spreading models from data, when the latter is incomplete or subject to uncertainty. |
Monday, March 6, 2023 8:48AM - 9:00AM |
A02.00003: Locating the source of forced oscillations in complex oscillator networks and power grids Melvyn Tyloo, Marc Vuffray, Andrey Y Lokhov, Robin Delabays Large-scale power grids are often subjected to forced oscillations originating from various faults or malfunctioning devices acting on the grid. On the long run, such oscillations might damage essential components which can eventually lead to major system failures and blackouts. It is therefore important to be able to locate the source of such forced oscillations in order to identify faulty network elements and mitigate their effect on the grid. With this task in mind, we developed an algorithm based on time-series measurements, that is able to identify both the source of the forced oscillation and its frequency. Importantly, it performs better than currently used methods. The method will be illustrated on synthetic and realistic data set. |
Monday, March 6, 2023 9:00AM - 9:12AM |
A02.00004: Deep Learning for Network Attack and Defense Jordan D Lanctot, Sean P Cornelius Networked systems are vulnerable to attacks that remove one or a few key nodes, causing the connectivity to collapse. Through the use of Neural Network (NN) embedding frameworks,[1] Deep Leaning (DL) can be used to discover vulnerable nodes that will cause network failure.[2] This capacity to discover key features of networks of varying sizes and degrees, brings into question the security and robustness of infrastructure and networked systems. Can the vulnerabilities of real networks be obfuscated through partial network concealment strategies, allowing for a defensive response? |
Monday, March 6, 2023 9:12AM - 9:24AM |
A02.00005: Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL) TANIA GHOSH, R. K. P. Zia, Kevin E Bassler Arguably the most fundamental problem in Network Science is finding structure within a complex network. One approach is to partition the nodes into communities that are more densely connected than one expects in a random network. "The" community structure corresponds to the partition that maximizes a measure that quantifies this idea. Finding the maximizing partition, however, is a computationally difficult NP-Complete problem. We explore the use of a recently introduced machine-learning algorithmic scheme [Guo, Singh, and Bassler, Sci. Rep. 9, 14234 (2019)] to find the structure of benchmark networks. The scheme, known as RenEEL, creates an ensemble of k partitions and updates the ensemble by replacing its worst member with the best of k' partitions found by analyzing a simplified network. The updating continues until consensus is achieved within the ensemble. Varying the values of k and k', we find that the results obey different classes of extreme value statistics and that increasing k is generally much more effective than increasing k' for finding the best partition. |
Monday, March 6, 2023 9:24AM - 9:36AM |
A02.00006: Information content and human perception of note transitions in music composed by Bach Suman Kulkarni, Sophia U David, Christopher W Lynn, Dani S Bassett Music has a complex structure that allows one to express emotion and convey information. How can the information in a musical composition be quantified? Prior research has shown that humans, when presented with information (such as a sequence of notes) do not process the information accurately. Here, we analyze Bach's music through the lens of network science and information theory. Regarded as one of the greatest composers, Bach's work spans a wide range of compositions and is highly mathematically structured. Viewing each composition as a network of note transitions, we show that Bach's music networks contain more information than random transition structures, and that different kinds of compositions can be grouped together according to their information content. Applying a free energy model for how humans infer the network structure of information that accounts for inaccuracies in perception, we observe that the inferred versions of Bach's music networks maintain a low deviation from their true network, implying they convey information efficiently. We probe the network structures that enable these properties --- namely, high heterogeneity and clustering. Our study sheds new light on the properties of Bach's compositions. More broadly, we gain insight into features that make networks of information effective for communication. |
Monday, March 6, 2023 9:36AM - 9:48AM |
A02.00007: Node-layer duality in multilayer networks Charley Presigny, Fabrizio De Vico Fallani Multilayer networks (MNs) constitute an efficient model paradigm for complex systems described over several interacting dimensions, levels or scales. |
Monday, March 6, 2023 9:48AM - 10:00AM |
A02.00008: Identifying network communities using higher-order structures Pramesh Singh, Hannah Kuder, Anna Ritz Traditional network community detection methods focus on identifying groups of nodes that contain more edges within the group than expected. However, real-world networks often exhibit rich topological structure beyond pairwise relationships, which is better characterized by motifs or graphlets. Thus, it is important to understand communities in terms of higher-order connectivity patterns. To this end, we introduce a graphlet-based community detection method that considers partitioning networks according to their high-order connectivity. Our approach provides a systematic way to obtain higher-order communities and offers a more descriptive view of network organization. When applied to a number of biological networks, we find that it detects functionally relevant groups that are not found by edge-based community detection. |
Monday, March 6, 2023 10:00AM - 10:12AM |
A02.00009: Prediction Market Miscalibration Reveals Aggregate Investor Behavior in Financial Networks Keanu M Rock, Sean P Cornelius, Jordan Lanctot In a prediction market (PM) investors trade contracts with payouts tied to real-world event outcomes, such as political races or sporting competitions. After many trades, contract prices are expected to converge to a value that accurately reflects the probability of an event outcome [1,2]. There are exceptions however; during the 2016 “Brexit’ referendum, PMs suggested a fait accompli for “Stay in the EU” despite polls suggesting a close contest [3,4]. Most research has focused on whether PMs correctly forecast the final (binary) outcome. But comparatively little attention has been paid to how well calibrated these markets are: do the implied probabilities of particular events represent the true odds over many realizations? |
Monday, March 6, 2023 10:12AM - 10:24AM |
A02.00010: Master stability function for frequency synchronization in laser networks Mostafa Honari Latifpour, Jiajie Ding, Igor Belykh, Mohammad-Ali Miri Network synchronization of lasers is of critical importance for reaching high-power levels as well as for effective optical computing. Yet, the role of network topology for frequency synchronization of lasers is not well understood. In this talk, we report our significant progress towards solving this critical problem for networks of heterogeneous lasers with repulsive coupling. We derive a master stability function for predicting the stability of frequency synchronization from the spectral knowledge of a complex matrix representing a combination of the signless Laplacian induced by the repulsive coupling and a matrix associated with intrinsic frequency detuning. We show that the gap between the two smallest eigenvalues of the complex matrix determines the coupling threshold for frequency synchronization. The application of our master stability function shows that, in stark contrast with Laplacian networks, dense long-range interactions do not necessarily lower the synchronization threshold. We reveal a non-trivial interplay between the network topology and geometric frustrations and demonstrate that full bi-partite networks have the optimal synchronization properties determined by the lowest synchronization threshold. Our results may provide guidelines for optimal designs of scalable laser networks capable of achieving reliable synchronization. |
Monday, March 6, 2023 10:24AM - 10:36AM |
A02.00011: Fractional centralities on networks: Consolidating the local and the global Kang-Ju Lee, Ki-Ahm Lee, Woong Kook, Taehun Lee We propose a new centrality incorporating two classical node-level centralities, the degree centrality and the information centrality, which are considered as local and global centralities, respectively. These two centralities have expressions in terms of the graph Laplacian $L$, which motivates us to exploit its fractional analog $L^{gamma}$ with a fractional parameter $gamma$. As $gamma$ varies from $0$ to $1$, the proposed fractional version of the information centrality makes intriguing changes in the node centrality rankings. These changes could not be generated by the fractional degree centrality since it is mostly influenced by the local aspect. We prove that these two fractional centralities behave similarly when $gamma$ is close to $0$. This result provides its complete understanding of the boundary of the interval in which $gamma$ lies since the fractional information centrality with $gamma=1$ is the usual information centrality. Moreover, our computation for the correlation coefficients between the fractional information centrality and the degree centrality reveals that the fractional information centrality is transformed from a local centrality into being a global one as $gamma$ changes from $0$ to $1$. |
Monday, March 6, 2023 10:36AM - 10:48AM |
A02.00012: Validating an algebraic approach to characterizing resonator networks Viva R Horowitz, Brittany E Carter, Uriel F Hernandez, Trevor A Scheuing, Benjamin J Aleman Resonator networks are ubiquitous in natural and engineered systems, such as solid-state materials, neural tissue, and electrical circuits. To understand and manipulate these networks—which are commonly modeled as systems of interacting mechanical resonators—it is essential to characterize their building blocks, which include the mechanical analogs of mass, elasticity, damping, and coupling of each resonator element. While these mechanical parameters are typically obtained from response spectra using least-squares fitting, this approach requires a priori knowledge of all parameters and is susceptible to large error due to convergence to local minima. Here we propose and validate an alternative algebraic means to characterize resonator networks with no or minimal a priori knowledge. Our approach recasts the equations of motion of the network into a linear homogeneous algebraic equation. We employ our approach on noisy simulated data from a single resonator and a coupled resonator pair, and we characterize the accuracy of the recovered parameters using high-dimension factorial simulations. Generally, we find the error is inversely proportional to the signal-to-noise ratio, that measurements at two frequencies are sufficient to recover all parameters, and sampling on the resonant peaks is optimal. Our simple, powerful tool will enable future efforts to ascertain and control resonator networks. |
Monday, March 6, 2023 10:48AM - 11:00AM |
A02.00013: Use of Transmission and Reflection Complex Time Delays to Reveal Scattering Matrix Poles and Zeros: Example of the Ring Graph Steven M Anlage, Lei Chen We identify the poles and zeros of the scattering matrix of a simple quantum graph by means of systematic measurement and analysis of Wigner, transmission, and reflection complex time delays [Lei Chen and S. M. Anlage, Phys Rev E 105, 054210 (2022)]. We examine the ring graph because it displays both shape and Feshbach resonances, the latter of which arises from an embedded eigenstate on the real frequency axis. Our analysis provides a unified understanding of the so-called shape, Feshbach, electromagnetically induced transparency, and Fano resonances on the basis of the distribution of poles and zeros of the scattering matrix in the complex frequency plane [Lei Chen, S. M. Anlage, and Yan V. Fyodorov, Phys Rev E 103, L050203 (2021)]. It also provides a first-principles understanding of sharp resonant scattering features and associated large time delay in a variety of practical devices, including photonic microring resonators, microwave ring resonators, and mesoscopic ring-shaped conductor devices. We also create a two-channel microwave graph realization of the Aharonov–Bohm ring, and demonstrate non-reciprocal transport in the presence of finite de-phasing/loss, in the intermediate regime between purely quantum and purely classical transport. |
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