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Paolo Boldi

Publications and source records attributed to Paolo Boldi.

At least 19 recordsLinked to original sources

The lattice of antichains of intervals: Dyck paths, level sets, and sweep dimension

The Clarke-Cormack-Burkowski lattice $\mathcal{E}_n$ is the lattice of antichains of intervals of the chain $\{0,1,\dots,n-1\}$, the algebraic structure underlying minimal-interval semantics, a query semantics used in information retrieval. We give an explicit, elementary anti-isomorphism between $\mathcal{E}_n$ minus its top element and the Stanley lattice of Dyck words of semilength $n+1$ (an anti-isomorphism whose existence is implicit in work of Ferrari and Munarini), under which the rank of an antichain is $\binom{n+1}{2}$ minus the area of the associated Dyck word. Hence the rank generating function of $\mathcal{E}_n\setminus\{\top\}$ is the reverse of the Carlitz-Riordan $q$-Catalan polynomial. Using a local limit theorem for the area of random-walk excursions, we confirm unconditionally an asymptotic formula of Takács: the size $λ_n$ of the largest level set of $\mathcal{E}_n$ satisfies $λ_n\simκ\,4^{n+1}/(n+1)^3$, where $κ\approx1.0807$. Finally, for the antichains of intervals of an arbitrary finite poset $P$ we study the sweep dimension, the minimum number of linear orders needed to separate every interval from every element outside it. It equals the Dushnik-Miller dimension of the lattice of convex subsets of $P$, it is the least $k$ for which $P$ maps into $\mathbb{R}^k$ so that every interval is the preimage of an upper orthant, and it lies between $\max(2,w)$ and $2w$, where $w$ is the width of $P$, both bounds being tight. Antichains of intervals embed, preserving joins, into $k$-dimensional staircase regions.

math.CO↗

The Hidden Geometry of Global Aid: How Money Moves Through 10 Million Transactions

International aid is usually described as flows between countries, hiding the organisations money passes through. We reconstruct global aid's organisational network from 10.9 million International Aid Transparency Initiative records, tracing over one million payments worth about US\$1 trillion between 22,773 organisations. First, centrality, measured as how far a funder's money can travel downstream, is not size. Relative to their number of partners, giant bilateral donors (USAID, UK FCDO) are only as central as expected, while the most central positions belong to foundations (Gates, Hewlett), research organisations (ODI, University of Oxford) and delivery contractors (Mott MacDonald). Second, principal component analysis of eleven flow measures separates organisations along three lines: whether they mainly give or receive, how much money flows in, and how reliant they are on few partners. Humanitarian organisations emerge from the flows alone, tending to handle larger inflows. Third, almost all organisations form one connected network, yet money moves down long, one-way chains. Among five comparison networks, aid most resembles international banking, with a few large hubs feeding many small players, but has far fewer loops and is more strictly top-down. This suggests a system with little redundancy, where losing a major funder or intermediary could cut off organisations downstream, a pressing question after recent donor cuts.

physics.soc-ph↗

Semi-Monotonicity for Spectral Centrality Measures

Score monotonicity and rank monotonicity are properties describing the behavior of a centrality measure when an arc is added to a network: the former requires that the score of the target of the arc should increase, the latter that its importance with respect to the remaining nodes should not deteriorate. While in directed networks almost all classical centrality measures satisfy both properties, in undirected networks they fail for most measures: adding an edge can reduce the score or the rank of one of its endpoints. Semi-monotonicity is a recently introduced weaker property for undirected networks, requiring that at least one of the two endpoints of the new edge enjoys monotonicity, and it is known to hold for closeness, harmonic centrality, distance-decay centralities and betweenness. In this paper we study semi-monotonicity for three classical spectral centrality measures: eigenvector centrality, Katz's index, and PageRank. We prove that all of them are strictly rank semi-monotone on connected undirected graphs, and that all of them are also score semi-monotone, (the only exception being eigenvector centrality when scores are normalized by projecting the constant vector on the dominant eigenspace, for which we provide a counterexample). In particular, adding an edge can never decrease the PageRank of both its endpoints, which answers a question left open in previous work.

cs.SI↗

M-Fibration Theory with Applications to Weighted Graphs

The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a commutative monoid. This is a genuine extension of the theory of graph fibrations (as introduced in "Fibrations of Graphs" [Discrete Math., vol. 243, pp. 21-66, 2002]), that makes it possible to deal with weighted graphs, and also graphs labelled with other algebraic structures. The derived theory also lends itself naturally to consider approximate fibrations. As an example, we show how the derived theory can be applied to the reduction of weighted networks, providing a strong theoretical underpinning to recent empirical results.

cs.LG↗

Recognizing Distance-Count Matrices is Difficult

Axiomatization of centrality measures often involves proving that something cannot hold by providing a counterexample (i.e., a graph for which that specific centrality index fails to have a given property). In the context of geometric centralities, building such counterexamples requires constructing a graph with specific distance counts between nodes, as expressed by its distance-count matrix. We prove that deciding whether a matrix is the distance-count matrix of a graph is strongly NP-complete. This negative result implies that a brute-force approach to building this kind of counterexample is out of question, and cleverer approaches are required.

cs.SI↗

Properties and Expressivity of Linear Geometric Centralities

Centrality indices are used to rank the nodes of a graph by importance: this is a common need in many concrete situations (social networks, citation networks, web graphs, for instance) and it was discussed many times in sociology, psychology, mathematics and computer science, giving rise to a whole zoo of definitions of centrality. Although they differ widely in nature, many centrality measures are based on shortest-path distances: such centralities are often referred to as geometric. Geometric centralities can use the shortest-path-length information in many different ways, but most of the existing geometric centralities can be defined as a linear transformation of the distance-count vector (that is, the vector containing, for every index t, the number of nodes at distance t). In this paper we study this class of centralities, that we call linear (geometric) centralities, in their full generality. In particular, we look at them in the light of the axiomatic approach, and we study their expressivity: we show to what extent linear centralities can be used to distinguish between nodes in a graph, and how many different rankings of nodes can be induced by linear centralities on a given graph. The latter problem (which has a number of possible applications, especially in an adversarial setting) is solved by means of a linear programming formulation, which is based on Farkas' lemma, and is interesting in its own right.

cs.SI↗

Symmetries of Living Systems: Symmetry Fibrations and Synchronization in Biological Networks

A symmetry is a `change without change'. As simple as it sounds, this concept is the fundamental cornerstone that unifies all branches of theoretical physics. Virtually all physical laws -- ranging from classical mechanics and electrodynamics to relativity, quantum mechanics, and the standard model -- can be expressed in terms of symmetry invariances. In this book, we explore whether the same principle can also explain the emergent laws of biological systems. We introduce a new geometry for biological networks and AI architectures, drawing inspiration from the mystic genius of Grothendieck's fibrations in category theory. We attempt to bridge the gap between physics and biology using symmetries but with a twist. The traditional symmetry groups of physics are global and too rigid to describe biology. Instead, the novel notion of symmetry fibration is local, flexible, and adaptable to evolutionary pressures, providing the right framework for understanding biological complexity. In other words, this more general symmetry invariance is necessary and sufficient to ensure that a given biological network configuration can support a synchronized function. In this book, we review the theoretical progress over the last decades from mathematics, physics, computer science, dynamical systems, and graph theory that has led to the discovery of symmetry fibrations in biological networks. These symmetries act as organizing principles for biological networks. They serve as effective tools for describing the structure of these networks, blending geometry and topology. Fibrations explain how structure dictates function across various biological domains, including the transcriptome, proteome, metabolome, and connectome. Additionally, they facilitate a reduction in the dimensionality of the network, simplifying it into its fundamental building blocks for biological computation.

q-bio.MN↗

Branching Ratios of Input Trees for Directed Multigraphs

We define the branching ratio of the input tree of a node in a finite directed multigraph, prove that it exists for every node, and show that it is equal to the largest eigenvalue of the adjacency matrix of the induced subgraph determined by all upstream nodes. This real eigenvalue exists by the Perron-Frobenius Theorem for non-negative matrices. We motivate our analysis with simple examples, obtain information about the asymptotics for the limit growth of the input tree, and establish other basic properties of the branching ratio.

math.CO↗

Fibration symmetry-breaking supports functional transitions in a brain network engaged in language

In his book 'A Beautiful Question', physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

q-bio.NC↗

The Role of Fibration Symmetries in Geometric Deep Learning

Geometric Deep Learning (GDL) unifies a broad class of machine learning techniques from the perspectives of symmetries, offering a framework for introducing problem-specific inductive biases like Graph Neural Networks (GNNs). However, the current formulation of GDL is limited to global symmetries that are not often found in real-world problems. We propose to relax GDL to allow for local symmetries, specifically fibration symmetries in graphs, to leverage regularities of realistic instances. We show that GNNs apply the inductive bias of fibration symmetries and derive a tighter upper bound for their expressive power. Additionally, by identifying symmetries in networks, we collapse network nodes, thereby increasing their computational efficiency during both inference and training of deep neural networks. The mathematical extension introduced here applies beyond graphs to manifolds, bundles, and grids for the development of models with inductive biases induced by local symmetries that can lead to better generalization.

cs.LG↗

Harmony in the Australian Domain Space

In this paper we use for the first time a systematic approach in the study of harmonic centrality at a Web domain level, and gather a number of significant new findings about the Australian web. In particular, we explore the relationship between economic diversity at the firm level and the structure of the Web within the Australian domain space, using harmonic centrality as the main structural feature. The distribution of harmonic centrality values is analyzed over time, and we find that the distributions exhibit a consistent pattern across the different years. The observed distribution is well captured by a partition of the domain space into six clusters; the temporal movement of domain names across these six positions yields insights into the Australian Domain Space and exhibits correlations with other non-structural characteristics. From a more global perspective, we find a significant correlation between the median harmonic centrality of all domains in each OECD country and one measure of global trust, the WJP Rule of Law Index. Further investigation demonstrates that 35 countries in OECD share similar harmonic centrality distributions. The observed homogeneity in distribution presents a compelling avenue for exploration, potentially unveiling critical corporate, regional, or national insights.

physics.soc-ph↗

Score and Rank Semi-Monotonicity for Closeness, Betweenness and Harmonic Centrality

In the study of the behavior of centrality measures with respect to network modifications, 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 of the target of the arc relative to the remaining nodes. It is known that score and rank monotonicity hold in directed graphs for almost all centrality measures. In undirected graphs one expects that the corresponding properties (where both endpoints of the new edge enjoy the increase in score/rank) hold when adding a new edge. However, recent results have shown that in undirected networks this is not true: for many centrality measures, it is possible to find situations where adding an edge reduces the rank of one of its two endpoints. In this paper we introduce a weaker condition for undirected networks, semi-monotonicity, in which just one of the endpoints of a new edge is required to enjoy score or rank monotonicity. We show that this condition is satisfied by closeness and betweenness centrality, and that harmonic centrality satisfies it in an even stronger sense.

cs.SI↗

MS-BioGraphs: Sequence Similarity Graph Datasets

Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i) investigate and optimize the process of generating large sequence similarity graphs as an HPC challenge and (ii) demonstrate this process in creating MS-BioGraphs, a new family of publicly available real-world edge-weighted graph datasets with up to $2.5$ trillion edges, that is, $6.6$ times greater than the largest graph published recently. The largest graph is created by matching (i.e., all-to-all similarity aligning) $1.7$ billion protein sequences. The MS-BioGraphs family includes also seven subgraphs with different sizes and direction types. We describe two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. We present a comparative study of structural characteristics of MS-BioGraphs. The datasets are available online on https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs .

cs.DC↗

Monotonicity in Undirected Networks

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 of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improve at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley's index, Katz's index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.

cs.SI↗

Spectral Rank Monotonicity on Undirected Networks

We study the problem of score and rank monotonicity for spectral ranking methods, such as eigenvector centrality and PageRank, in the case of undirected networks. Score monotonicity means that adding an edge increases the score at both ends of the edge. Rank monotonicity means that adding an edge improves the relative position of both ends of the edge with respect to the remaining nodes. It is known that common spectral rankings are both score and rank monotone on directed, strongly connected graphs. We show that, surprisingly, the situation is very different for undirected graphs, and in particular that PageRank is neither score nor rank monotone.

cs.SI↗

Quasifibrations of Graphs to Find Symmetries in Biological Networks

A fibration of graphs is an homomorphism that is a local isomorphism of in-neighbourhoods, much in the same way a covering projection is a local isomorphism of neighbourhoods. Recently, it has been shown that graph fibrations are useful tools to uncover symmetries and synchronization patterns in biological networks ranging from gene, protein,and metabolic networks to the brain. However, the inherent incompleteness and disordered nature of biological data precludes the application of the definition of fibration as it is; as a consequence, also the currently known algorithms to identify fibrations fail in these domains. In this paper, we introduce and develop systematically the theory of quasifibrations which attempts to capture more realistic patterns of almost-synchronization of units in biological networks. We provide an algorithmic solution to the problem of finding quasifibrations in networks where the existence of missing links and variability across samples preclude the identification of perfect symmetries in the connectivity structure. We test the algorithm against other strategies to repair missing links in incomplete networks using real connectome data and synthetic networks. Quasifibrations can be applied to reconstruct any incomplete network structure characterized by underlying symmetries and almost synchronized clusters.

q-bio.QM↗

Fine-Grained Network Analysis for Modern Software Ecosystems

Modern software development is increasingly dependent on components, libraries and frameworks coming from third-party vendors or open-source suppliers and made available through a number of platforms (or forges). This way of writing software puts an emphasis on reuse and on composition, commoditizing the services which modern applications require. On the other hand, bugs and vulnerabilities in a single library living in one such ecosystem can affect, directly or by transitivity, a huge number of other libraries and applications. Currently, only product-level information on library dependencies is used to contain this kind of danger, but this knowledge often reveals itself too imprecise to lead to effective (and possibly automated) handling policies. We will discuss how fine-grained function-level dependencies can greatly improve reliability and reduce the impact of vulnerabilities on the whole software ecosystem.

cs.SE↗

Fast Prefix Search in Little Space, with Applications

It has been shown in the indexing literature that there is an essential difference between prefix/range searches on the one hand, and predecessor/rank searches on the other hand, in that the former provably allows faster query resolution. Traditionally, prefix search is solved by data structures that are also dictionaries---they actually contain the strings in $S$. For very large collections stored in slow-access memory, we propose much more compact data structures that support \emph{weak} prefix searches---they return the ranks of matching strings provided that \emph{some} string in $S$ starts with the given prefix. In fact, we show that our most space-efficient data structure is asymptotically space-optimal. Previously, data structures such as String B-trees (and more complicated cache-oblivious string data structures) have implicitly supported weak prefix queries, but they all have query time that grows logarithmically with the size of the string collection. In contrast, our data structures are simple, naturally cache-efficient, and have query time that depends only on the length of the prefix, all the way down to constant query time for strings that fit in one machine word. We give several applications of weak prefix searches, including exact prefix counting and approximate counting of tuples matching conjunctive prefix conditions.

cs.DS↗