Past Networks Seminar

Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from ∼20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the average attention afforded to national Covid-19 mortality rates is modelled accurately with the Weber–Fechner and power law functions of sensory perception. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.

The science of cities seeks to understand and explain regularities observed in the world's major urban systems. Modelling the population evolution of cities is at the core of this science and of all urban studies. Quantitatively, the most fundamental problem is to understand the hierarchical organization of cities and the statistical occurrence of megacities, first thought to be described by a universal law due to Zipf, but whose validity has been challenged by recent empirical studies. A theoretical model must also be able to explain the relatively frequent rises and falls of cities and civilizations, and despite many attempts these fundamental questions have not been satisfactorily answered yet. Here we fill this gap by introducing a new kind of stochastic equation for modelling population growth in cities, which we construct from an empirical analysis of recent datasets (for Canada, France, UK and USA) that reveals how rare but large interurban migratory shocks dominate city growth. This equation predicts a complex shape for the city distribution and shows that Zipf's law does not hold in general due to finite-time effects, implying a more complex organization of cities. It also predicts the existence of multiple temporal variations in the city hierarchy, in agreement with observations. Our result underlines the importance of rare events in the evolution of complex systems and at a more practical level in urban planning.

arXiv link: https://arxiv.org/abs/2011.09403

Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding, or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization -- which is characterized by modular structure and heavy-tailed degrees -- facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network's structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

arXiv link: https://arxiv.org/abs/2011.08994

Empirical networks often exhibit different meso-scale structures, such as community and core-periphery structure. Core-periphery typically consists of a well-connected core, and a periphery that is well-connected to the core but sparsely connected internally. Most core-periphery studies focus on undirected networks. In this talk we discuss  a generalisation of core-periphery to directed networks which  yields a family of core-periphery blockmodel formulations in which, contrary to many existing approaches, core and periphery sets are edge-direction dependent. Then we shall  focus on a particular structure consisting of two core sets and two periphery sets, and we introduce  two measures to assess the statistical significance and quality of this  structure in empirical data, where one often has no ground truth. The idea will be illustrated on three empirical networks --  faculty hiring, a world trade data-set, and political blogs.

This is based on joint work with Andrew Elliott, Angus Chiu, Marya Bazzi and Mihai Cucuringu, available at https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2019.0783

Networks are an imperfect representation of a dataset, yet often there is little consideration for how these imperfections may affect network evolution and structure.

In this talk, I want to discuss a simple set of generative network models in which the mechanism of network growth is decomposed into two layers. The first layer represents the “observed” network, corresponding to our conventional understanding of a network. Here I want to consider the scenario in which the network you observe is not self-contained, but is driven by a second hidden network, comprised of the same nodes but different edge structure. I will show how a range of different network growth models can be constructed such that the observed and hidden networks can be causally decoupled, coupled only in one direction, or coupled in both directions.

One consequence of such models is the emergence of abrupt transitions in observed network topology – one example results in scale-free degree distributions which are robust up to an arbitrarily long threshold time, but which naturally break down as the network grows larger. I will argue that such examples illustrate why we should be wary of an overreliance on static networks (measured at only one point in time), and will discuss other possible implications for prediction on networks.

Multibody interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. We derive and analyze models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multibody dynamical effects that go beyond rescaled pairwise interactions can appear only if the interaction function is nonlinear, regardless of the underlying multibody structure. As a practical application, we introduce a specific nonlinear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure, and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarized clusters where multibody interactions make one cluster dominate the other.

Building on these results, we generalise the model allowing for interactions within hyper edges of any cardinality and explore in detail the role of involvement and stubbornness on polarisation.

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference needed for credible causal inference. Existing solutions include accounting for the fraction or count of treated neighbors in a user's network, among other strategies. Yet, there are often a high number of researcher degrees of freedom in specifying network interference conditions and most current methods do not account for the local network structure beyond simply counting the number of neighbors. Capturing local network structures is important because it can account for theories, such as structural diversity and echo chambers. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the assignment conditions of neighbors. We propose a two-part approach. We first introduce and employ "causal network motifs," i.e. network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree-based algorithm for identifying different network interference conditions and estimating their average potential outcomes. We test our method on a real-world experiment on a large-scale network and a synthetic network setting, which highlight how accounting for local structures can better account for different interference patterns in networks.

(joint work with T. Kania, Academy of Sciences of the Czech Republic, Prague)
In this talk we discuss our recent result on the inverse eigenvalue problem for symmetric doubly stochastic matrices. 
Namely, we provide a new sufficient condition for a list of real numbers to be the spectrum of a symmetric doubly stochastic matrix. 
In our construction of such matrices, we employ the eigenvectors of the transition probability matrix of a simple symmetric random walk on the circle. 
We also demonstrate a simple algorithm for generating random doubly stochastic matrices based on our construction. Examples will be provided.

Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network's connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B's partition relative to A's own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows the for the generation of graphs with extensive numbers of triangles and other network motifs commonly observed in many real world networks. More specifically we focus on maximum entropy ensembles under constraints placed on the counts and distributions of atomic subgraphs and derive general expressions for the entropy of such models. We also present a procedure for combining distributions of multiple atomic subgraphs that enables the construction of models with fewer parameters. Expanding the model to include atoms with edge and vertex labels we obtain a general class of models that can be parametrized in terms of basic building blocks and their distributions that includes many widely used models as special cases. These models include random graphs with arbitrary distributions of subgraphs, random hypergraphs, bipartite models, stochastic block models, models of multilayer networks and their degree corrected and directed versions. We show that the entropy for all these models can be derived from a single expression that is characterized by the symmetry groups of atomic subgraphs.

Modular and hierarchical structures are pervasive in real-world complex systems. A great deal of effort has gone into trying to detect and study these structures. Important theoretical advances in the detection of modular, or "community", structures have included identifying fundamental limits of detectability by formally defining community structure using probabilistic generative models. Detecting hierarchical community structure introduces additional challenges alongside those inherited from community detection. Here we present a theoretical study on hierarchical community structure in networks, which has thus far not received the same rigorous attention. We address the following questions: 1) How should we define a valid hierarchy of communities? 2) How should we determine if a hierarchical structure exists in a network? and 3) how can we detect hierarchical structure efficiently? We approach these questions by introducing a definition of hierarchy based on the concept of stochastic externally equitable partitions and their relation to probabilistic models, such as the popular stochastic block model. We enumerate the challenges involved in detecting hierarchies and, by studying the spectral properties of hierarchical structure, present an efficient and principled method for detecting them.

https://arxiv.org/abs/2009.07196 (15 sept.)

We develop a theory to measure the variance and covariance of probability distributions defined on the nodes of a graph, which takes into account the distance between nodes. Our approach generalizes the usual (co)variance to the setting of weighted graphs and retains many of its intuitive and desired properties. As a particular application, we define the maximum-variance problem on graphs with respect to the effective resistance distance, and characterize the solutions to this problem both numerically and theoretically. We show how the maximum-variance distribution can be interpreted as a core-periphery measure, illustrated by the fact that these distributions are supported on the leaf nodes of tree graphs, low-degree nodes in a configuration-like graph and boundary nodes in random geometric graphs. Our theoretical results are supported by a number of experiments on a network of mathematical concepts, where we use the variance and covariance as analytical tools to study the (co-)occurrence of concepts in scientific papers with respect to the (network) relations between these concepts. Finally, I will draw connections to related notion of assortativity on networks, a network analogue of correlation used to describe how the presence and absence of edges covaries with the properties of nodes.

https://arxiv.org/abs/2008.09155

Systems with lattice geometry can be renormalized exploiting their embedding in metric space, which naturally defines the coarse-grained nodes. By contrast, complex networks defy the usual techniques because of their small-world character and lack of explicit metric embedding. Current network renormalization approaches require strong assumptions (e.g. community structure, hyperbolicity, scale-free topology), thus remaining incompatible with generic graphs and ordinary lattices. Here we introduce a graph renormalization scheme valid for any hierarchy of coarse-grainings, thereby allowing for the definition of block-nodes across multiple scales. This approach reveals a necessary and specific dependence of network topology on an additive hidden variable attached to nodes, plus optional dyadic factors. Renormalizable networks turn out to be consistent with a unique specification of the fitness model, while they are incompatible with preferential attachment, the configuration model or the stochastic blockmodel. These results highlight a deep conceptual distinction between scale-free and scale-invariant networks, and provide a geometry-free route to renormalization. If the hidden variables are annealed, the model spontaneously leads to realistic scale-free networks with cut-off. If they are quenched, the model can be used to renormalize real-world networks with node attributes and distance-dependence or communities. As an example we derive an accurate multiscale model of the International Trade Network applicable across arbitrary geographic resolutions.

https://arxiv.org/abs/2009.11024 (23 sept.)

We consider random graph models where graphs are generated by connecting not only pairs of nodes by edges but also larger subsets of
nodes by copies of small atomic subgraphs of arbitrary topology. More specifically we consider canonical and microcanonical ensembles
corresponding to constraints placed on the counts and distributions of atomic subgraphs and derive general expressions for the entropy of such
models. We also introduce a procedure that enables the distributions of multiple atomic subgraphs to be combined resulting in more coarse
grained models. As a result we obtain a general class of models that can be parametrized in terms of basic building blocks and their
distributions that includes many widely used models as special cases. These models include random graphs with arbitrary distributions of subgraphs (Karrer & Newman PRE 2010, Bollobas et al. RSA 2011), random hypergraphs, bipartite models, stochastic block models, models of multilayer networks and their degree corrected and directed versions. We show that the entropy expressions for all these models can be derived from a single expression that is characterized by the symmetry groups of their atomic subgraphs.

Many real networks feature the property of nestedness, i.e. the neighbours of nodes with a few connections are hierarchically nested within the neighbours of nodes with more connections. Despite the abstract simplicity of this notion, different mathematical definitions of nestedness have been proposed, sometimes giving contrasting results. Moreover, there is an ongoing debate on the statistical significance of nestedness, since even random networks where the number of connections (degree) of each node is fixed to its empirical value are typically as nested as real-world ones. In this talk we show unexpected effects due to the recent finding that random networks where the degrees are enforced as hard constraints (microcanonical ensembles) are thermodynamically different from random networks where the degrees are enforced as soft constraints (canonical ensembles). We show that if the real network is perfectly nested, then the two ensembles are trivially equivalent and the observed nestedness, independently of its definition, is indeed an unavoidable consequence of the empirical degrees. On the other hand, if the real network is not perfectly nested, then the two ensembles are not equivalent and alternative definitions of nestedness can be even positively correlated in the canonical ensemble and negatively correlated in the microcanonical one. This result disentangles distinct notions of nestedness captured by different metrics and highlights the importance of making a principled choice between hard and soft constraints in null models of ecological networks.

[1] Bruno, M., Saracco, F., Garlaschelli, D., Tessone, C. J., & Caldarelli, G. (2020). Nested mess: thermodynamics disentangles conflicting notions of nestedness in ecological networks. arXiv preprint arXiv:2001.11805.
 

The notion of emergence is at the core of many of the most challenging open scientific questions, being so much a cause of wonder as a perennial source of philosophical headaches. Two classes of emergent phenomena are usually distinguished: strong emergence, which corresponds to supervenient properties with irreducible causal power; and weak emergence, which are properties generated by the lower levels in such "complicated" ways that they can only be derived by exhaustive simulation. While weak emergence is generally accepted, a large portion of the scientific community considers causal emergence to be either impossible, logically inconsistent, or scientifically irrelevant.

In this talk we present a novel, quantitative framework that assesses emergence by studying the high-order interactions of the system's dynamics. By leveraging the Integrated Information Decomposition (ΦID) framework [1], our approach distinguishes two types of emergent phenomena: downward causation, where macroscopic variables determine the future of microscopic degrees of freedom; and causal decoupling, where macroscopic variables influence other macroscopic variables without affecting their corresponding microscopic constituents. Our framework also provides practical tools that are applicable on a range of scenarios of practical interest, enabling to test -- and possibly reject -- hypotheses about emergence in a data-driven fashion. We illustrate our findings by discussing minimal examples of emergent behaviour, and present a few case studies of systems with emergent dynamics, including Conway’s Game of Life, neural population coding, and flocking models.
[1] Mediano, Pedro AM, Fernando Rosas, Robin L. Carhart-Harris, Anil K. Seth, and Adam B. Barrett. "Beyond integrated information: A taxonomy of information dynamics phenomena." arXiv preprint arXiv:1909.02297 (2019).
 

Spatial networks are often the most natural way to represent spatial information of different kinds. One of the outstanding problems in current spatial network research is to effectively quantify the heterogeneity of the discrete-valued spatial distributions underlying a spatial graph. In this talk we will presentsome recent alternative approaches to estimate heterogeneity in spatial networks based on simple dynamical processes running on them.

Multilayer networks are a way to represent dependent connectivity patterns — e.g., time-dependence, multiple types of interactions, or both — that arise in many applications and which are difficult to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as communities, to discover features that lie between the microscale and the macroscale. We introduce a framework for the construction of generative models for mesoscale structure in multilayer networks.  We model dependency at the level of partitions rather than with respect to edges, and treat the process of generating a multilayer partition separately from the process of generating edges for a given multilayer partition. Our framework can admit many features of empirical multilayer networks and explicitly incorporates a user-specified interlayer dependency structure. We discuss the parameters and some properties of our framework, and illustrate an example of its use with benchmark models for multilayer community-detection tools. 

Real networks are highly clustered (large number of short cycles) in contrast with their random counterparts. The Erdős–Rényi model and the Configuration model will generate networks with a tree like structure, a feature rarely observed in real networks. This means that traditional random networks are a poor choice as null models for real networks. Can we do better than that? Maximum entropy random graph ensembles are the natural choice to generate such networks. By introducing a bias with respect to the number of short cycles in a degree constrained graph, we aim to get a random graph model with a tuneable number of short cycles [1,2]. Nevertheless, the story is not so simple. In the same way random unclustered graphs present undesired topology, highly clustered ones will do as well if one is not careful with the scaling of the control parameters relative to the system size. Additionally the techniques to generate and sample numerically from general biased degree constrained graph ensembles will also be discussed. The topological transition has an important impact on the computational cost to sample graphs from these ensembles. To take it one step further, a general approach using the eigenvalues of the adjacency matrix rather than just the number of short cycles will also be presented, [2].

[1] López, Fabián Aguirre, et al. "Exactly solvable random graph ensemble with extensively many short cycles." Journal of Physics A: Mathematical and Theoretical 51.8 (2018): 085101.
[2] López, Fabián Aguirre, and Anthony CC Coolen. "Imaginary replica analysis of loopy regular random graphs." Journal of Physics A: Mathematical and Theoretical 53.6 (2020): 065002.

Random graphs were introduced as a convenient example for demonstrating the impossibility of ‘complete disorder’ by Erdos, who also thought that these objects will never become useful in the applied areas outside of pure mathematics. In this talk, I will view random graphs as objects in the field of applied mathematics and discus how the application-driven objectives have set new directions for studying random graphs. I will focus on characterising the sizes of connected components in graphs with a given degree distribution, on the percolation-like processes on such structures, and on generalisations to the coloured graphs. These theoretical questions have interesting implications for studying resilience of networks with nontrivial structures, and for materials science where they explain kinetics-driven phase transitions. Even more surprisingly, the results reveal intricate connections between random graphs and non-linear partial differential equations indicating new possibilities for their analysis.

Can spatial fungal networks be informative for both ecology and network science?

Filamentous organisms grow as adaptive biological spatial networks. These networks are in a continuous balance of two main forces: exploration of the habitat to acquire scarce resources, and the transport of those resources within the developing network. In addition, the construction of the network has to be kept a low cost while taking into account the risk of damage by predation. Such network optimization is not unique to biological systems, but is relevant to transport networks across many domains. Thus, this collaborative project between FU-Berlin and University of Oxford represents the beginning of a research program that aims at: First, setting up protocols for the use of network analysis to characterize spatial networks formed by both macroscopic and microscopic filamentous organisms (e.g. Fungi), and determining the fitness and ecological consequences of different structure of the networks. Second, extracting biologically-inspired algorithms that lead to optimized network formation in fungi and discuss their utility in other network domains. This information is critical to demonstrate that we have a viable and scalable pipeline for the measurement of such properties as well provide preliminary evidence of the usefulness of studying network properties of fungi.