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Algorithmic aspects of temporal betweenness Network Science Pub Date : 2024-04-12 Sebastian Buß, Hendrik Molter, Rolf Niedermeier, Maciej Rymar
The betweenness centrality of a graph vertex measures how often this vertex is visited on shortest paths between other vertices of the graph. In the analysis of many real-world graphs or networks, the betweenness centrality of a vertex is used as an indicator for its relative importance in the network. In particular, it is among the most popular tools in social network analysis. In recent years, a
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When can networks be inferred from observed groups? Network Science Pub Date : 2024-04-12 Zachary P. Neal
Collecting network data directly from network members can be challenging. One alternative involves inferring a network from observed groups, for example, inferring a network of scientific collaboration from researchers’ observed paper authorships. In this paper, I explore when an unobserved undirected network of interest can accurately be inferred from observed groups. The analysis uses simulations
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Generating preferential attachment graphs via a Pólya urn with expanding colors Network Science Pub Date : 2024-04-08 Somya Singh, Fady Alajaji, Bahman Gharesifard
We introduce a novel preferential attachment model using the draw variables of a modified Pólya urn with an expanding number of colors, notably capable of modeling influential opinions (in terms of vertices of high degree) as the graph evolves. Similar to the Barabási-Albert model, the generated graph grows in size by one vertex at each time instance; in contrast however, each vertex of the graph is
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A generalized hypothesis test for community structure in networks Network Science Pub Date : 2024-03-11 Eric Yanchenko, Srijan Sengupta
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing
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Methodological moderators of average outdegree centrality: A meta-analysis of child and adolescent friendship networks Network Science Pub Date : 2024-03-08 Jennifer Watling Neal
Empirical articles vary considerably in how they measure child and adolescent friendship networks. This meta-analysis examines four methodological moderators of children’s and adolescents’ average outdegree centrality in friendship networks: boundary specification, operational definition of friendship, unlimited vs. fixed choice design, and roster vs. free recall design. Specifically, multi-level random
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Automated detection of edge clusters via an overfitted mixture prior Network Science Pub Date : 2024-01-19 Hanh T. D. Pham, Daniel K. Sewell
Most community detection methods focus on clustering actors with common features in a network. However, clustering edges offers a more intuitive way to understand the network structure in many real-life applications. Among the existing methods for network edge clustering, the majority are algorithmic, with the exception of the latent space edge clustering (LSEC) model proposed by Sewell (Journal of
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Audience selection for maximizing social influence Network Science Pub Date : 2024-01-12 Balázs R. Sziklai, Balázs Lengyel
Viral marketing campaigns target primarily those individuals who are central in social networks and hence have social influence. Marketing events, however, may attract diverse audience. Despite the importance of event marketing, the influence of heterogeneous target groups is not well understood yet. In this paper, we define the Audience Selection (AS) problem in which different sets of agents need
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Reengineering of interbank networks Network Science Pub Date : 2023-12-18 John Leventides, Costas Poulios, Maria Livada, Ioannis Giannikos
We investigate the reengineeering of interbank networks with a specific focus on capital increase. We consider a scenario where all other components of the network’s infrastructure remain stable (a practical assumption for short-term situations). Our objective is to assess the impact of raising capital on the network’s robustness and to address the following key aspects. First, given a predefined target
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Graph-based methods for discrete choice Network Science Pub Date : 2023-11-06 Kiran Tomlinson, Austin R. Benson
Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence
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Business transactions and ownership ties between firms Network Science Pub Date : 2023-10-16 László Lőrincz, Sándor Juhász, Rebeka O. Szabó
In this study, we investigate the creation and persistence of interfirm ties in a large-scale business transaction network. Business transaction relations (firms buying or selling products or services to each other) are driven by economic motives, but because trust is essential to business relationships, the social connections of owners or the geographical proximity of firms can also influence their
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Do NBA teams avoid trading within their own division? Network Science Pub Date : 2023-09-18 jimi adams, Michał Bojanowski
Within US professional sports, trades within one’s own division are often perceived to be disadvantageous. We ask how common this practice is. To examine this question, we construct a date-stamped network of all trades in the National Basketball Association between June 1976 and May 2019. We then use season-specific weighted exponential random graph models to estimate the likelihood of teams avoiding
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Typologies of duocentric networks among low-income newlywed couples Network Science Pub Date : 2023-08-25 David P. Kennedy, Thomas N. Bradbury, Benjamin R. Karney
The social networks surrounding intimate couples provide them with bonding and bridging social capital and have been theorized to be associated with their well-being and relationship quality. These networks are multidimensional, featuring compositional (e.g., the proportion of family members vs. friends) and structural characteristics (e.g., density, degree of overlap between spouses’ networks). Most
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Colorful path detection in vertex-colored temporal Network Science Pub Date : 2023-08-18 Riccardo Dondi, Mohammad Mehdi Hosseinzadeh
Finding paths is a fundamental problem in graph theory and algorithm design due to its many applications. Recently, this problem has been considered on temporal graphs, where edges may change over a discrete time domain. The analysis of graphs has also taken into account the relevance of vertex properties, modeled by assigning to vertices labels or colors. In this work, we deal with a problem that
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Limited evidence for structural balance in the family Network Science Pub Date : 2023-08-17 Jonas Stein, Jornt Mandemakers, Arnout van de Rijt
Previous studies have shown that relationship sentiments in families follow a pattern wherein either all maintain positive relationships or there are two antagonistic factions. This result is consistent with the network theory of structural balance that individuals befriend their friends’ friend and become enemies with their friends’ enemies. Fault lines in families would then endogenously emerge through
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Continuous latent position models for instantaneous interactions Network Science Pub Date : 2023-07-24 Riccardo Rastelli, Marco Corneli
We create a framework to analyze the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays and easily available. Examples of instantaneous interactions include email networks, phone call networks, and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent
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A general model for how attributes can reduce polarization in social groups Network Science Pub Date : 2023-07-24 Piotr J. Górski, Curtis Atkisson, Janusz A. Hołyst
Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating
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Regression of binary network data with exchangeable latent errors Network Science Pub Date : 2023-07-03 Frank W. Marrs, Bailey K. Fosdick
Undirected, binary network data consist of indicators of symmetric relations between pairs of actors. Regression models of such data allow for the estimation of effects of exogenous covariates on the network and for prediction of unobserved data. Ideally, estimators of the regression parameters should account for the inherent dependencies among relations in the network that involve the same actor.
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Transitions between peace and systemic war as bifurcations in a signed network dynamical system Network Science Pub Date : 2023-06-21 Megan Morrison, J. Nathan Kutz, Michael Gabbay
We investigate structural features and processes associated with the onset of systemic conflict using an approach which integrates complex systems theory with network modeling and analysis. We present a signed network model of cooperation and conflict dynamics in the context of international relations between states. The model evolves ties between nodes under the influence of a structural balance force
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Exact recovery of Granger causality graphs with unconditional pairwise tests Network Science Pub Date : 2023-06-06 R. J. Kinnear, R. R. Mazumdar
We study Granger Causality in the context of wide-sense stationary time series. The focus of the analysis is to understand how the underlying topological structure of the causality graph affects graph recovery by means of the pairwise testing heuristic. Our main theoretical result establishes a sufficient condition (in particular, the graph must satisfy a polytree assumption we refer to as strong causality)
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Relational event models in network science Network Science Pub Date : 2023-05-08 Carter T. Butts, Alessandro Lomi, Tom A. B. Snijders, Christoph Stadtfeld
Relational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes
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Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models Network Science Pub Date : 2023-05-08 Jason Gravel, Matthew Valasik, Joris Mulder, Roger Leenders, Carter Butts, P. Jeffrey Brantingham, George E. Tita
The hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain
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Modeling complex interactions in a disrupted environment: Relational events in the WTC response Network Science Pub Date : 2023-04-18 Scott Leo Renshaw, Selena M. Livas, Miruna G. Petrescu-Prahova, Carter T. Butts
When subjected to a sudden, unanticipated threat, human groups characteristically self-organize to identify the threat, determine potential responses, and act to reduce its impact. Central to this process is the challenge of coordinating information sharing and response activity within a disrupted environment. In this paper, we consider coordination in the context of responses to the 2001 World Trade
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Segregated mobility patterns amplify neighborhood disparities in the spread of COVID-19 Network Science Pub Date : 2023-04-17 Andras Gyorgy, Thomas Marlow, Bruno Abrahao, Kinga Makovi
The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate
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How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models Network Science Pub Date : 2023-04-04 Giuseppe Arena, Joris Mulder, Roger Th. A.J. Leenders
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as
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The duality of networks and groups: Models to generate two-mode networks from one-mode networks Network Science Pub Date : 2023-03-20 Zachary P. Neal
Shared memberships, social statuses, beliefs, and places can facilitate the formation of social ties. Two-mode projections provide a method for transforming two-mode data on individuals’ memberships in such groups into a one-mode network of their possible social ties. In this paper, I explore the opposite process: how social ties can facilitate the formation of groups, and how a two-mode network can
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A network community detection method with integration of data from multiple layers and node attributes Network Science Pub Date : 2023-03-07 Hannu Reittu, Lasse Leskelä, Tomi Räty
Multilayer networks are in the focus of the current complex network study. In such networks, multiple types of links may exist as well as many attributes for nodes. To fully use multilayer—and other types of complex networks in applications, the merging of various data with topological information renders a powerful analysis. First, we suggest a simple way of representing network data in a data matrix
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Searching for coherence in a fragmented field: Temporal and keywords network analysis in political science Network Science Pub Date : 2023-02-13 Dmitry G. Zaytsev, Valentina V. Kuskova, Gregory S. Khvatsky, Anna A. Sokol
In this paper, we answer the multiple calls for systematic analysis of paradigms and subdisciplines in political science—the search for coherence within a fragmented field. We collected a large dataset of over seven hundred thousand writings in political science from Web of Science since 1946. We found at least two waves of political science development, from behaviorism to new institutionalism. Political
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Random effects in dynamic network actor models Network Science Pub Date : 2023-02-06 Alvaro Uzaheta, Viviana Amati, Christoph Stadtfeld
Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational events is the outcome of an actor-oriented decision process consisting of two decision levels. The first level represents the time until an actor initiates the next relational event, modeled by an exponential distribution with an actor-specific activity rate. The second level describes the choice of the receiver of
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Monotonicity in undirected networks Network Science Pub Date : 2023-02-02 Paolo Boldi, Flavio Furia, Sebastiano Vigna
Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance
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A simplest mathematics of turn-taking: Conversational deep structure, emergence, and permeation Network Science Pub Date : 2023-01-25 Bryan C. Cannon, Dawn T. Robinson
David Gibson’s (2008) examination of research on conversational interaction highlighted methodological and theoretical gaps in current understanding – particularly around the localized construction of interaction and the reproduction of social structures. This paper extends extant formal models used by group process researchers to explain how exogenous status structures shape local interaction by incorporating
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Preferential attachment hypergraph with high modularity Network Science Pub Date : 2023-01-23 Frédéric Giroire, Nicolas Nisse, Thibaud Trolliet, Małgorzata Sulkowska
Numerous works have been proposed to generate random graphs preserving the same properties as real-life large-scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist, and also, just a few models allow to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present
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Expanding the boundaries of interdisciplinary field: Contribution of Network Science journal to the development of network science Network Science Pub Date : 2023-01-20 Valentina V. Kuskova, Dmitry G. Zaytsev, Gregory S. Khvatsky, Anna A. Sokol, Maria D. Vorobeva, Rustam A. Kamalov
In this paper, we examine the contribution of Network Science journal to the network science discipline. We do so from two perspectives. First, expanding the existing taxonomy of article contribution, we examine trends in theory testing, theory building, and new method development within the journal’s articles. We find that the journal demands a high level of theoretical contribution and methodological
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Quality issues in co-authorship data of a national scientific community Network Science Pub Date : 2023-01-20 Domenico De Stefano, Vittorio Fuccella, Maria Prosperina Vitale, Susanna Zaccarin
A stream of research on co-authorship, used as a proxy of scholars’ collaborative behavior, focuses on members of a given scientific community defined at discipline and/or national basis for which co-authorship data have to be retrieved. Recent literature pointed out that international digital libraries provide partial coverage of the entire scholar scientific production as well as under-coverage of
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Toward random walk-based clustering of variable-order networks Network Science Pub Date : 2022-12-22 Julie Queiros, Célestin Coquidé, François Queyroi
Higher-order networks aim at improving the classical network representation of trajectories data as memory-less order $1$ Markov models. To do so, locations are associated with different representations or “memory nodes” representing indirect dependencies between visited places as direct relations. One promising area of investigation in this context is variable-order network models as it was suggested
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Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity Network Science Pub Date : 2022-12-15 Alina Lungeanu, Ryan Whalen, Y. Jasmine Wu, Leslie A. DeChurch, Noshir S. Contractor
Despite the importance of diverse expertise in helping solve difficult interdisciplinary problems, measuring it is challenging and often relies on proxy measures and presumptive correlates of actual knowledge and experience. To address this challenge, we propose a text-based measure that uses researcher’s prior work to estimate their substantive expertise. These expertise estimates are then used to
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Multimodal mechanisms of political discourse dynamics and the case of Germany’s nuclear energy phase-out Network Science Pub Date : 2022-12-15 Sebastian Haunss, James Hollway
The 2011 policy pivot of the German government, from extending nuclear power plants terms to securing their shutdown for 2022, cannot be explained without looking at how the German political discourse network shifted in the months following Fukushima. This paper seeks to model and identify mechanisms that help explain how the two-mode network of political actors’ support for claims developed. We identify
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Micro-level network dynamics of scientific collaboration and impact: Relational hyperevent models for the analysis of coauthor networks Network Science Pub Date : 2022-11-28 Jürgen Lerner, Marian-Gabriel Hâncean
We discuss a recently proposed family of statistical network models—relational hyperevent models (RHEMs)—for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication
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Efficiently generating geometric inhomogeneous and hyperbolic random graphs Network Science Pub Date : 2022-11-23 Thomas Bläsius, Tobias Friedrich, Maximilian Katzmann, Ulrich Meyer, Manuel Penschuck, Christopher Weyand
Hyperbolic random graphs (HRGs) and geometric inhomogeneous random graphs (GIRGs) are two similar generative network models that were designed to resemble complex real-world networks. In particular, they have a power-law degree distribution with controllable exponent $\beta$ and high clustering that can be controlled via the temperature $T$ .We present the first implementation of an efficient GIRG
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Understanding collaboration patterns on funded research projects: A network analysis Network Science Pub Date : 2022-11-23 Matthew Smith, Yasaman Sarabi, Dimitris Christopoulos
This paper provides an examination of inter-organizational collaboration in the UK research system. Data are collected on organizational collaboration on projects funded by four key UK research councils: Arts and Humanities Research Council, Economic and Social Research Council, Engineering and Physical Sciences Research Council, and Biotechnology and Biological Sciences Research Council. The organizational
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Random networks grown by fusing edges via urns Network Science Pub Date : 2022-11-03 Kiran R. Bhutani, Ravi Kalpathy, Hosam Mahmoud
Many classic networks grow by hooking small components via vertices. We introduce a class of networks that grows by fusing the edges of a small graph to an edge chosen uniformly at random from the network. For this random edge-hooking network, we study the local degree profile, that is, the evolution of the average degree of a vertex over time. For a special subclass, we further determine the exact
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A multi-purposed unsupervised framework for comparing embeddings of undirected and directed graphs Network Science Pub Date : 2022-09-28 Bogumił Kamiński, Łukasz Kraiński, Paweł Prałat, François Théberge
Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately
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All that glitters is not gold: Relational events models with spurious events Network Science Pub Date : 2022-09-16 Cornelius Fritz, Marius Mehrl, Paul W. Thurner, Göran Kauermann
As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence,
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Block dense weighted networks with augmented degree correction Network Science Pub Date : 2022-09-14 Benjamin Leinwand, Vladas Pipiras
Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node’s community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed
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Strong and weak tie homophily in adolescent friendship networks: An analysis of same-race and same-gender ties Network Science Pub Date : 2022-09-14 Cassie McMillan
While we know that adolescents tend to befriend peers who share their race and gender, it is unclear whether patterns of homophily vary according to the strength, intimacy, or connectedness of these relationships. By applying valued exponential random graph models to a sample of 153 adolescent friendship networks, I test whether tendencies towards same-race and same-gender friendships differ for strong
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How teams adapt to exogenous shocks: Experimental evidence with node knockouts of central members Network Science Pub Date : 2022-09-13 Jared F. Edgerton, Skyler J. Cranmer, Victor Finomore
Researchers have found that although external attacks, exogenous shocks, and node knockouts can disrupt networked systems, they rarely lead to the system’s collapse. Although these processes are widely understood, most studies of how exogenous shocks affect networks rely on simulated or observational data. Thus, little is known about how groups of real individuals respond to external attacks. In this
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Connectivity-preserving distributed algorithms for removing links in directed networks Network Science Pub Date : 2022-09-06 Azwirman Gusrialdi
This article considers the link removal problem in a strongly connected directed network with the goal of minimizing the dominant eigenvalue of the network’s adjacency matrix while maintaining its strong connectivity. Due to the complexity of the problem, this article focuses on computing a suboptimal solution. Furthermore, it is assumed that the knowledge of the overall network topology is not available
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Techniques for blocking the propagation of two simultaneous contagions over networks using a graph dynamical systems framework Network Science Pub Date : 2022-08-30 Henry L. Carscadden, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz
We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations
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Generating weighted and thresholded gene coexpression networks using signed distance correlation Network Science Pub Date : 2022-06-16 Javier Pardo-Diaz, Philip S. Poole, Mariano Beguerisse-Díaz, Charlotte M. Deane, Gesine Reinert
Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming
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Bringing network science to primary school Network Science Pub Date : 2022-05-30 Clara Stegehuis
Several papers have highlighted the potential of network science to appeal to a younger audience of high school children and provided lesson material on network science for high school children. However, network science also provides a great topic for outreach activities for primary school children. Therefore, this article gives a short summary of an outreach activity on network science for primary
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Consensus embedding for multiple networks: Computation and applications Network Science Pub Date : 2022-05-30 Mengzhen Li, Mustafa Coşkun, Mehmet Koyutürk
Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex”
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A hierarchical latent space network model for mediation Network Science Pub Date : 2022-05-30 Tracy M. Sweet, Samrachana Adhikari
For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for
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A pairwise strategic network formation model with group heterogeneity: With an application to international travel Network Science Pub Date : 2022-05-27 Tadao Hoshino
This study considers a network formation model in which each dyad of agents strategically determines the link status. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood method, in which the latent group structure is estimated using the binary segmentation algorithm in the second
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Network classification-based structural analysis of real networks and their model-generated counterparts Network Science Pub Date : 2022-05-20 Marcell Nagy, Roland Molontay
Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine-learning techniques to investigate the aforementioned
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Editors’ Note Network Science Pub Date : 2022-04-22 Stanley Wasserman, Ulrik Brandes
We welcome our new editors and provide background on an unusual duo of articles in this issue.
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Role detection in bicycle-sharing networks using multilayer stochastic block models Network Science Pub Date : 2022-04-22 Jane Carlen, Jaume de Dios Pont, Cassidy Mentus, Shyr-Shea Chang, Stephanie Wang, Mason A. Porter
In urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete
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DERGMs: Degeneracy-restricted exponential family random graph models Network Science Pub Date : 2022-03-31 Vishesh Karwa, Sonja Petrović, Denis Bajić
Exponential random graph models, or ERGMs, are a flexible and general class of models for modeling dependent data. While the early literature has shown them to be powerful in capturing many network features of interest, recent work highlights difficulties related to the models’ ill behavior, such as most of the probability mass being concentrated on a very small subset of the parameter space. This
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Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer Network Science Pub Date : 2022-03-10 Per Block, James Hollway, Christoph Stadtfeld, Johan Koskinen, Tom Snijders
We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in this journal [Network Science 7(1):20–51, 2019]. When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks’ observed degrees instead of endogenous ERGM equivalents of structural effects as used in the
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A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum Network Science Pub Date : 2022-03-01 Philip Leifeld,Skyler J. Cranmer
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The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests Network Science Pub Date : 2022-03-01 Philip Leifeld,Skyler J. Cranmer
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Faster MCMC for Gaussian latent position network models Network Science Pub Date : 2022-02-22 Neil A. Spencer, Brian W. Junker, Tracy M. Sweet
Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node’s latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it