Stephany Rajeh , Hocine Cherifi

Rajeh S, Cherifi H (2024) On the role of diffusion dynamics on community-aware centrality measures. PLoS ONE 19(7): e0306561. https://doi.org/10.1371/journal.pone.0306561

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology and finance to biology and ecology. The presence of a community structure within real-world networks has a substantial impact on how diffusion processes unfold. Key nodes located both within and between these communities play a crucial role in initiating diffusion, and community-aware centrality measures effectively identify these nodes. While numerous diffusion models have been proposed in literature, very few studies investigate the relationship between the diffusive ability of key nodes selected by community-aware centrality measures, the distinct dynamical conditions of various models, and the diverse network topologies. By conducting a comparative evaluation across four diffusion models, utilizing both synthetic and real-world networks, along with employing two different community detection techniques, our study aims to gain deeper insights into the effectiveness and applicability of the community-aware centrality measures. Results suggest that the diffusive power of the selected nodes is affected by three main factors: the strength of the network’s community structure, the internal dynamics of each diffusion model, and the budget availability. Specifically, within the category of simple contagion models, such as SI, SIR, and IC, we observe similar diffusion patterns when the network’s community structure strength and budget remain constant. In contrast, the LT model, which falls under the category of complex contagion dynamics, exhibits divergent behavior under the same conditions.

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Guillaume Moinard, Matthieu Latapy

In: Luca Maria Aiello, Tanmoy Chakraborty, Sabrina Gaito (eds) 16th International Conference, ASONAM 2024, Rende, Italy, September 2–5, 2024, Proceedings, Part IV

doi : TBA

We propose a simple model of protesters scattered throughout a city who want to gather into large and mobile groups. This model relies on random walkers on a street network that follow tactics built from a set of basic rules. Our goal is to identify the most important rules for fast and robust flocking of walkers. We explore a wide set of tactics and show the central importance of a specific rule based on alignment. Other rules alone perform poorly, but our experiments show that combining alignment with them enhances flocking, and that obtained groups are then remarkably robust.

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Duncan Cassells, Lionel Tabourier, Pedro Ramaciotti

In: Botta, F., Macedo, M., Barbosa, H., Menezes, R. (eds) Complex Networks XV. CompleNet-Live 2024. Springer Proceedings in Complexity. Springer, Cham.

https://doi.org/10.1007/978-3-031-57515-0_4

The study of polarization has gained increasing attraction in the past decades. Since observing both opinions and interactions is challenging, epistemic programs such as agent-based models have been proposed as a means to assessing the systemic consequences of social psychology mechanisms. Most results in agent-based models for opinion dynamics have focused on individual opinion constructs and pairwise interactions, with a few works treating group effects as constraints. Meanwhile, a tradition in social sciences has been putting emphasis on how group configuration affects individual behavior. In this work, we introduce a new model for accounting for both pairwise interactions in which actors observe and update opinions, and individual perception of the evolving configuration of groups that make up the population in which they are embedded. Through experiments, we show that pairwise interactions which are different depending on whether they are in-group or out-group, has quantifiable impact on the resulting polarization of a population. In particular, the tolerance toward out-group opinions is shown to have a strong impact on the resulting polarization. Our model produces and accounts for polarized states resulting from group consolidation and fragmentation.

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Bastien Legay, Matthieu Latapy

In: Botta, F., Macedo, M., Barbosa, H., Menezes, R. (eds) Complex Networks XV. CompleNet-Live 2024. Springer Proceedings in Complexity. Springer, Cham.

https://doi.org/10.1007/978-3-031-57515-0_1

We consider the following problem: we have a high-resolution street network of a given city, and low-resolution measurements of traffic within this city. We want to associate to each measurement the set of streets corresponding to the observed traffic. To do so, we take benefit of specific properties of these data to match measured links to links in the street network. We propose several success criteria for the obtained matching. They show that the matching algorithm generally performs very well, and they give complementary ways to detect data discrepancies that makes any matching highly dubious.

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Alexis Baudin, Clémence Magnien, Lionel Tabourier

preprint arXiv:2405.04428

Bipartite graphs are a prevalent modeling tool for real-world networks, capturing interactions between vertices of two different types. Within this framework, bicliques emerge as crucial structures when studying dense subgraphs: they are sets of vertices such that all vertices of the first type interact with all vertices of the second type. Therefore, they allow identifying groups of closely related vertices of the network, such as individuals with similar interests or webpages with similar contents. This article introduces a new algorithm designed for the exhaustive enumeration of maximal bicliques within a bipartite graph. This algorithm, called BBK for Bipartite Bron-Kerbosch, is a new extension to the bipartite case of the Bron-Kerbosch algorithm, which enumerates the maximal cliques in standard (non-bipartite) graphs. It is faster than the state-of-the-art algorithms and allows the enumeration on massive bipartite graphs that are not manageable with existing implementations. We analyze it theoretically to establish two complexity formulas: one as a function of the input and one as a function of the output characteristics of the algorithm. We also provide an open-access implementation of BBK in C++, which we use to experiment and validate its efficiency on massive real-world datasets and show that its execution time is shorter in practice than state-of-the art algorithms. These experiments also show that the order in which the vertices are processed, as well as the choice of one of the two types of vertices on which to initiate the enumeration have an impact on the computation time.

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Alexis Baudin, Clémence Magnien, Lionel Tabourier

Journal of Graph Algorithms and Applications, Vol. 28 No. 1 (2024), p. 149-178

Link streams offer a good model for representing interactions over time. They consist of links (b,e,u,v), where u and v are vertices interacting during the whole time interval [b,e]. In this paper, we deal with the problem of enumerating maximal cliques in link streams. A clique is a pair (C,[t0,t1]), where C is a set of vertices that all interact pairwise during the full interval [t0,t1]. It is maximal when neither its set of vertices nor its time interval can be increased. Some of the main works solving this problem are based on the famous Bron-Kerbosch algorithm for enumerating maximal cliques in graphs. We take this idea as a starting point to propose a new algorithm which matches the cliques of the instantaneous graphs formed by links existing at a given time t to the maximal cliques of the link stream. We prove its validity and compute its complexity, which is better than the state-of-the art ones in many cases of interest. We also study the output-sensitive complexity, which is close to the output size, thereby showing that our algorithm is efficient. To confirm this, we perform experiments on link streams used in the state of the art, and on massive link streams, up to 100 million links. In all cases our algorithm is faster, mostly by a factor of at least 10 and up to a factor of 10**4. Moreover, it scales to massive link streams for which the existing algorithms are not able to provide the solution.

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Lionel Tabourier, Julien Karadayi

In Journal of Complex Networks, 2024, 12 (1)

DOI: 10.1093/comnet/cnae002

Generating graphs with realistic structural characteristics is an important challenge for complex networks analysis, as these graphs are the null models that allow to describe and understand the properties of real-world networks. However, the field lacks systematic Generating graphs with realistic structural characteristics is an important challenge for complex networks analysis, as these graphs are the null models that allow to describe and understand the properties of real-world networks. However, the field lacks systematic means to generate samples of graphs with predefined structural properties, because it is difficult to devise a method that is both flexible and guarantees to get a uniform sample, i.e., where any graph of the target set has the same probability to be represented in the sample. In practice, it limits the experimental investigation to a handful of models, including the well-known Erdős-Rényi graphs or the configuration model. The aim of this paper is to provide such a method: we design and implement a Monte-Carlo Markov Chain process which is both flexible and satisfies the uniformity condition. Its assumptions are that: 1) the graphs are simple, 2) their degree sequence is fixed, 3) the user has at least one graph of the set available. Within these limitations, we prove that it is possible to generate a uniform sample of any set of such graphs. We provide an implementation in python and extensive experiments to show that this method is practically operational in several relevant cases. We use it with five specific set of constraints and verify that the samples obtained are consistent with existing methods when such a method is available. In those cases, we report that state-of-the-art methods are usually faster, as our method favors versatility at the cost of a lower efficiency. Also, the implementation provided has been designed so that users may adapt it to relevant constraints for their own field of work.

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Esteban Bautista, Matthieu Latapy

In « Temporal Network Theory », Holme, P. and Saramäki, J. (eds), Springer, 2023

DOI: 10.1007/978-3-031-30399-9_22œ

A link stream is a set of possibly weighted triplets (t, u, v) modeling that u and v interacted at time t. Link streams offer an effective model for datasets containing both temporal and relational information, making their proper analysis crucial in many applications. They are commonly regarded as sequences of graphs or collections of time series. Yet, a recent seminal work demonstrated that link streams are more general objects of which graphs are only particular cases. It therefore started the construction of a dedicated formalism for link streams by extending graph theory. In this work, we contribute to the development of this formalism by showing that link streams also generalize time series. In particular, we show that a link stream corresponds to a time-series extended to a relational dimension, which opens the door to also extend the framework of signal processing to link streams. We therefore develop extensions of numerous signal concepts to link streams: from elementary ones like energy, correlation, and differentiation, to more advanced ones like Fourier transform and filters.

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Frédéric Simard, Clémence Magnien, Matthieu Latapy

Journal of Graph Algorithms and Applications 27:3, 2023 DOI: 10.7155/jgaa.00620

Betweenness centrality is one of the most important concepts in graph analysis. It was recently extended to link streams, a graph generalization where links arrive over time. However, its computation raises non-trivial issues, due in particular to the fact that time is considered as continuous. We provide here the first algorithms to compute this generalized betweenness centrality, as well as several companion algorithms that have their own interest. They work in polynomial time and space, we illustrate them on typical examples, and we provide an implementation.

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Code available here

Esteban Bautista, Matthieu Latapy

Fifth IEEE International Conference on Cognitive Machine Intelligence (CogMI), 2023

A link stream is a set of triplets (t,u,v) indicating that u and v interacted at time t. Link streams model numerous datasets and their proper study is crucial in many applications. In practice, raw link streams are often aggregated or transformed into time series or graphs where decisions are made. Yet, it remains unclear how the dynamical and structural information of a raw link stream carries into the transformed object. This work shows that it is possible to shed light into this question by studying link streams via algebraically linear graph and signal operators, for which we introduce a novel linear matrix framework for the analysis of link streams. We show that, due to their linearity, most methods in signal processing can be easily adopted by our framework to analyze the time/frequency information of link streams. However, the availability of linear graph methods to analyze relational/structural information is limited. We address this limitation by developing (i) a new basis for graphs that allow us to decompose them into structures at different resolution levels; and (ii) filters for graphs that allow us to change their structural information in a controlled manner. By plugging-in these developments and their time-domain counterpart into our framework, we are able to (i) obtain a new basis for link streams that allow us to represent them in a frequency-structure domain; and (ii) show that many interesting transformations to link streams, like the aggregation of interactions or their embedding into a euclidean space, can be seen as simple filters in our frequency-structure domain.

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Alexis Baudin, Lionel Tabourier, Clémence Magnien

30th International Symposium on Temporal Representation and Reasoning (TIME 2023)

Community detection is a popular approach to understand the organization of interactions in static networks. For that purpose, the Clique Percolation Method (CPM), which involves the percolation of k-cliques, is a well-studied technique that offers several advantages. Besides, studying interactions that occur over time is useful in various contexts, which can be modeled by the link stream formalism. The Dynamic Clique Percolation Method (DCPM) has been proposed for extending CPM to temporal networks.
However, existing implementations are unable to handle massive datasets. We present a novel algorithm that adapts CPM to link streams, which has the advantage that it allows us to speed up the computation time with respect to the existing DCPM method. We evaluate it experimentally on real datasets and show that it scales to massive link streams. For example, it allows to obtain a complete set of communities in under twenty-five minutes for a dataset with thirty million links, what the state of the art fails to achieve even after a week of computation. We further show that our method provides communities similar to DCPM, but slightly more aggregated. We exhibit the relevance of the obtained communities in real world cases, and show that they provide information on the importance of vertices in the link streams.

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Alexis Baudin, Clémence Magnien, Lionel Tabourier

EGC 2023, vol. RNTI-E-39, pp.139-150

Les flots de liens offrent un formalisme de description d’interactions au cours du temps. Un lien correspond à deux sommets qui interagissent sur un intervalle de temps. Une clique est un ensemble de sommets associé à un intervalle de temps durant lequel ils sont tous connectés. Elle est maximale si ni son ensemble de sommets ni son intervalle de temps ne peuvent être augmentés. Les algorithmes existants pour énumérer ces structures ne permettent pas de traiter des jeux de données réels de plus de quelques centaines de milliers d’interactions. Or, l’accès à des données toujours plus massives demande d’adapter les outils à de plus grandes échelles. Nous proposons alors un algorithme qui énumère les cliques maximales sur des réseaux temporels réels et massifs atteignant jusqu’à plus de 100 millions de liens. Nous montrons expérimentalement qu’il améliore l’état de l’art de plusieurs ordres de grandeur.

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Fabrice Lécuyer, Louis Jachiet, Clémence Magnien, Lionel Tabourier

ALENEX 2023

Listing triangles is a fundamental graph problem with many applications, and large graphs require fast algorithms. Vertex ordering allows the orientation of edges from lower to higher vertex indices, and state-of-the-art triangle listing algorithms use this to accelerate their execution and to bound their time complexity. Yet, only basic orderings have been tested. In this paper, we show that studying the precise cost of algorithms instead of their bounded complexity leads to faster solutions. We introduce cost functions that link ordering properties with the running time of a given algorithm. We prove that their minimization is NP-hard and propose heuristics to obtain new orderings with different trade-offs between cost reduction and ordering time. Using datasets with up to two billion edges, we show that our heuristics accelerate the listing of triangles by an average of 38% when the ordering is already given as an input, and 16% when the ordering time is included.

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Antoine Genitrini, Mehdi Naima, Olivier Bodini

The 15th Latin American Theoretical Informatics Symposium (LATIN 2022)

In this paper we study a model of Schr ̈oder trees whose labelling is increasing along the branches. Such tree family is useful in the context of phylogenetic. The tree nodes are of arbitrary arity (i.e. out-degree) and the node labels can be repeated throughout different branches of the tree. Once a formal construction of the trees is formalized, we then turn to the enumeration of the trees inspired by a renormalisation due to Stanley on acyclic orientations of graphs. We thus exhibit links between our tree model and labelled graphs and prove a one-to-one correspondence between a subclass of our trees and labelled graphs. As a by-product we obtain a new natural combinatorial interpretation of Stanley’s renormalising factor. We then study different combinatorial characteristics of our tree model and finally, we design an efficient uniform random sampler for our tree model which allows to generate uniformly Erdös-Renyi graph with a constant number of rejections on
average.

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Maximilien Danisch, Ioannis Panagiotas, Lionel Tabourier

arXiv:2209.12062

In order to manage massive graphs in practice, it is often necessary to resort to graph compression, which aims at reducing the memory used when storing and processing the graph. Efficient compression methods have been proposed in the literature, especially for web graphs. In most cases, they are combined with a vertex reordering pre-processing step which significantly improves the compression rate. However, these techniques are not as efficient when considering other kinds of graphs. In this paper, we focus on the class of bipartite graphs and adapt the vertex reordering phase to their specific structure by proposing a dual reordering scheme. By reordering each group of vertices in the purpose of minimizing a specific score, we show that we can reach better compression rates. We also suggest that this approach can be further refined to make the node orderings more adapted to the compression phase that follows the ordering phase.

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Nouamane Arhachoui, Esteban Bautista, Maximilien Danisch, Anastasios Giovanidis

ASONAM 2022

Abstract—Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as $\psi$-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The $\psi$-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of N users it requires to solve N linear systems of equations of size N. To address this problem, this work introduces a novel scalable algorithm for the fast approximation of $\psi$-score, named Power-$\psi$. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size N to compute the $\psi$-score. Then, our algorithm exploits the fact that such system can be recursively and distributedly approximated to any desired error. This permits the $\psi$-score, summarizing both structural and behavioral information for the nodes, to run as fast as PageRank. We validate the effectiveness of the proposed algorithm on several real-world datasets.

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Esteban Bautista, Matthieu Latapy

In Social Network Analysis and Mining, 2022, vol. 12, no 1, p. 1-11.

The personalized PageRank algorithm is one of the most versatile tools for the analysis of networks. In spite of its ubiquity, maintaining personalized PageRank vectors when the underlying network constantly evolves is still a challenging task. To address this limitation, this work proposes a novel distributed algorithm to locally update personalized PageRank vectors when the graph topology changes. The proposed algorithm is based on the use of Chebyshev polynomials and a novel update equation that encompasses a large family of PageRank-based methods. In particular, the algorithm has the following advantages: (i) it has faster convergence speed than state-of-the-art alternatives for local PageRank updating; and (ii) it can update the solution of recent generalizations of PageRank for which no updating algorithms have been developed. Experiments in a real-world temporal network of an autonomous system validate the effectiveness of the proposed algorithm.

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Alexis Baudin, Maximilien Danisch, Sergey Kirgizov, Clémence Magnien

International Conference on Advanced Data Mining and Applications (ADMA), 2021

Automatic detection of relevant groups of nodes in large real-world graphs, i.e. community detection, has applications in many fields and has received a lot of attention in the last twenty years. The most popular method designed to find overlapping communities (where a node can belong to several communities) is perhaps the clique percolation method (CPM). This method formalizes the notion of community as a maximal union of k-cliques that can be reached from each other through a series of adjacent k-cliques, where two cliques are adjacent if and only if they overlap on k-1 nodes. Despite much effort CPM has not been scalable to large graphs for medium values of k. Recent work has shown that it is possible to efficiently list all k-cliques in very large real-world graphs for medium values of k. We build on top of this work and scale up CPM. In cases where this first algorithm faces memory limitations, we propose another algorithm, CPMZ, that provides a solution close to the exact one, using more time but less memory.

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Esteban Bautista, Matthieu Latapy

In 10th International Conference on Complex Networks and their Applications, Madrid (Spain), December 2021 (Poster)

Many systems generate data as a set of triplets (a,b,c): they may represent that user a
called b at time c or that customer a purchased product b in store c. These datasets are
traditionally studied as networks with an extra dimension (time or layer), for which the
fields of temporal and multiplex networks have extended graph theory to account for
the new dimension [1]. However, such frameworks detach one variable from the others
and allow to extend one same concept in many ways, making it hard to capture pat-
terns across all dimensions and to identify the best definitions for a given dataset. This
work overrides this vision and proposes a direct processing of the set of triplets. While
[2] also approaches triplets directly, it focuses on specific patterns and applications.
Our work shows that a more general analysis is possible by partitioning the data and
building categorical propositions (CPs) that encode informative patterns. We show that
several concepts from graph theory can be framed under this formalism and leverage
such insights to extend the concepts to data triplets. Lastly, we propose an algorithm to
list CPs satisfying specific constraints and apply it to a real world dataset.

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Johannes Langguth, Ioannis Panagiotas, Bora Uçar

28th edition of the IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2021), Dec 2021, Bangalore, India

We investigate the parallelization of the Karp-Sipser kernelization technique, which constitutes the central part of the well-known Karp-Sipser heuristic for the maximum cardinality matching problem. The technique reduces a given problem instance to a smaller but equivalent one, by repeated applications of two operations: vertex removal, and merging two vertices. The operation of merging two vertices poses the principal challenge in parallelizing the technique. We describe an algorithm that minimizes the need for synchronization and present an efficient shared-memory parallel implementation of the kernelization technique for bipartite graphs. Using extensive experiments on a variety of multicore CPUs, we show that our implementation scales well up to 32 cores on one socket.

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