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We propose a bond-percolation model intended to describe the consumption, and eventual exhaustion, of resources in transport networks. Edges forming minimum-length paths connecting demanded origin-destination nodes are removed if below a certain budget. As pairs of nodes are demanded and edges are removed, the macroscopic connected component of the graph disappears, i.e., the graph undergoes a percolation transition. Here, we study such a shortest-path-percolation transition in homogeneous random graphs where pairs of demanded origin-destination nodes are randomly generated, and fully characterize it by means of finite-size scaling analysis. If budget is finite, the transition is identical to the one of ordinary percolation, where a single giant cluster shrinks as edges are removed from the graph; for infinite budget, the transition becomes more abrupt than the one of ordinary percolation, being characterized by the sudden fragmentation of the giant connected component into a multitude of clusters of similar size., Comment: 5 pages, 5 figures, 1 table + Supplemental Material

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations for a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper of network science. Here, we develop a machine learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques., Comment: 12 pages, 10 figures, 2 tables

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements., Comment: 5 pages, 2 figures, KDD 2023 Machine Learning in Finance workshop

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects., Comment: 8 pages, 3 figures, forthcoming in ICWSM23

When an unprecedented infectious disease with high mortality and transmissibility emerges, immediate usage of vaccines or medicines is hardly available. Thus, many health authorities rely on non-pharmaceutical interventions through traceable fixed contacts. However, in reality, there is an additional type of transmission routes to the regular and fixed contacts: the random anonymous infection cases where non-pharmaceutical interventions are hardly feasible. In our study, such realistic situations are implemented by the susceptible-infected-recovered model with isolation on multiplex networks. The multiplex networks are composed of a fixed interaction layer and a layer with time-varying random interactions to represent the different types of disease spreading routes. The multiplex networks represent the combinations of the quenched disorder and annealed disorder. Here, we suggest a preemptive isolation protocol which isolates the second nearest neighbors of the hospitalized individuals and compare it with one of the most popular protocol adopted by many health organizations over the globe. From numerical simulations we find that our preemptive measure significantly reduces both the final epidemic size and the number of the isolated per unit time. Our finding suggests a better non-pharmaceutical intervention which can be adopted to various types of diseases even though the contact tracing is only partially available., Comment: 19 pages, 12 figures, LaTex; title changed, typos corrected, section "Summary Paragraph" changed into "Abstract", removed references in abstract/ v3: manuscript is revised

Sequences of polygons generated by performing iterative processes on an initial polygon have been studied extensively. One of the most popular sequences is the one sometimes referred to as {\it Kasner polygons}. Given a polygon $K$, the first Kasner descendant $K'$ of $K$ is obtained by placing the vertices of $K'$ at the midpoints of the edges of $K$. More generally, for any fixed $m$ in $(0,1)$ one may define a sequence of polygons $\{K^{t}\}_{t\ge 0}$ where each polygon $K^{t}$ is obtained by dividing every edge of $K^{t-1}$ into the ratio $m:(1-m)$ in the counterclockwise (or clockwise) direction and taking these division points to be the vertices of $K^{t}$. We are interested in the following problem {\it Let $m$ be a fixed number in $(0,1)$ and let $n\ge 3$ be a fixed integer. Further, let $K$ be a convex $n$-gon and denote by $K'$, the first $m$-Kasner descendant of $K$, that is, the vertices of $K'$ divide the edges of $K$ into the ratio $m:(1-m)$. What can be said about the ratio between the area of $K'$ and the area of $K$, when $K$ varies in the class of convex $n$-gons?} We provide a complete answer to this question., Comment: 20 pages, 10 figures