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MOUFLON: Multi-group Modularity-based Fairness-aware Community Detection
Authors:
Georgios Panayiotou,
Anand Mathew Muthukulam Simon,
Matteo Magnani,
Ece Calikus
Abstract:
In this paper, we propose MOUFLON, a fairness-aware, modularity-based community detection method that allows adjusting the importance of partition quality over fairness outcomes. MOUFLON uses a novel proportional balance fairness metric, providing consistent and comparable fairness scores across multi-group and imbalanced network settings. We evaluate our method under both synthetic and real netwo…
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In this paper, we propose MOUFLON, a fairness-aware, modularity-based community detection method that allows adjusting the importance of partition quality over fairness outcomes. MOUFLON uses a novel proportional balance fairness metric, providing consistent and comparable fairness scores across multi-group and imbalanced network settings. We evaluate our method under both synthetic and real network datasets, focusing on performance and the trade-off between modularity and fairness in the resulting communities, along with the impact of network characteristics such as size, density, and group distribution. As structural biases can lead to strong alignment between demographic groups and network structure, we also examine scenarios with highly clustered homogeneous groups, to understand how such structures influence fairness outcomes. Our findings showcase the effects of incorporating fairness constraints into modularity-based community detection, and highlight key considerations for designing and benchmarking fairness-aware social network analysis methods.
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Submitted 14 October, 2025;
originally announced October 2025.
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Multimodal Coordinated Online Behavior: Trade-offs and Strategies
Authors:
Lorenzo Mannocci,
Stefano Cresci,
Matteo Magnani,
Anna Monreale,
Maurizio Tesconi
Abstract:
Coordinated online behavior, which spans from beneficial collective actions to harmful manipulation such as disinformation campaigns, has become a key focus in digital ecosystem analysis. Traditional methods often rely on monomodal approaches, focusing on single types of interactions like co-retweets or co-hashtags, or consider multiple modalities independently of each other. However, these approa…
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Coordinated online behavior, which spans from beneficial collective actions to harmful manipulation such as disinformation campaigns, has become a key focus in digital ecosystem analysis. Traditional methods often rely on monomodal approaches, focusing on single types of interactions like co-retweets or co-hashtags, or consider multiple modalities independently of each other. However, these approaches may overlook the complex dynamics inherent in multimodal coordination. This study compares different ways of operationalizing multimodal coordinated behavior, examining the trade-off between weakly and strongly integrated models and their ability to capture broad versus tightly aligned coordination patterns. By contrasting monomodal, flattened, and multimodal methods, we evaluate the distinct contributions of each modality and the impact of different integration strategies. Our findings show that while not all modalities provide unique insights, multimodal analysis consistently offers a more informative representation of coordinated behavior, preserving structures that monomodal and flattened approaches often lose. This work enhances the ability to detect and analyze coordinated online behavior, offering new perspectives for safeguarding the integrity of digital platforms.
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Submitted 13 February, 2026; v1 submitted 16 July, 2025;
originally announced July 2025.
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Detecting Coordinated Behaviour on Video-First Platforms: The Challenge of Multimodality and Complex Similarity on TikTok
Authors:
Inga K. Wohlert,
Davide Vega,
Matteo Magnani,
Alexandra Segerberg
Abstract:
Research on online coordinated behaviour has predominantly focused on text-based social media platforms. However, the rise of video-first platforms such as TikTok introduces distinct challenges. The multimodal nature of video posts, combining visuals, audio, and text, allows for coordination across various modalities and complicates comparison between posts. This paper proposes an approach to dete…
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Research on online coordinated behaviour has predominantly focused on text-based social media platforms. However, the rise of video-first platforms such as TikTok introduces distinct challenges. The multimodal nature of video posts, combining visuals, audio, and text, allows for coordination across various modalities and complicates comparison between posts. This paper proposes an approach to detecting coordination that addresses these characteristic challenges. Our methodology, based on multilayer network analysis, is tailored to capture coordination across multiple modalities, and explicitly handles complex forms of similarity inherent in video and audio content. We test this approach on German political posts regarding the 2024 European Elections retrieved via the TikTok Research API. Our results demonstrate the ability of our approach to identify coordination within the constraints of the API, while also critically highlighting potential pitfalls and limitations.
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Submitted 28 October, 2025; v1 submitted 6 June, 2025;
originally announced June 2025.
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Aspirational Affordances of AI
Authors:
Sina Fazelpour,
Meica Magnani
Abstract:
As artificial intelligence (AI) systems increasingly permeate processes of cultural and epistemic production, there are growing concerns about how their outputs may confine individuals and groups to restricted narratives about who or what they could be. In this paper, we advance the discourse surrounding these concerns by making three contributions. First, we introduce the concept of aspirational…
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As artificial intelligence (AI) systems increasingly permeate processes of cultural and epistemic production, there are growing concerns about how their outputs may confine individuals and groups to restricted narratives about who or what they could be. In this paper, we advance the discourse surrounding these concerns by making three contributions. First, we introduce the concept of aspirational affordance to describe how culturally shared interpretive resources, such as concepts, images, and narratives, can shape individual cognition, and in particular exercises of imagination. We show the usefulness of this concept for grounding the evaluation of psychological risks posed by AI. Second, we provide three reasons for scrutinizing AI's influence on aspirational affordances: AI's influence is potentially more potent, but less public, than that of traditional sources; the influence is not simply incremental, but ecological, transforming the entire landscape of practices that shape aspirational affordances; and it is highly concentrated, with a few corporate-controlled systems mediating a growing portion of production. Our third contribution is to advance such a scrutiny of AI's influence by introducing the concept of aspirational harm. In the context of AI systems, such harms arise when AI-enabled aspirational affordances distort or diminish available interpretive resources in ways that undermine individuals' ability to imagine relevant practical possibilities. Through three case studies, we illustrate how aspirational harms extend the existing discourse on AI-inflicted harms beyond representational and allocative harms, warranting separate attention. Overall, this paper aims to advance our understanding of the psychological and societal stakes of AI in shaping individual and collective aspirations.
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Submitted 19 August, 2026; v1 submitted 21 April, 2025;
originally announced April 2025.
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An Image is Worth $K$ Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
Authors:
Matías Piqueras,
Alexandra Segerberg,
Matteo Magnani,
Måns Magnusson,
Nataša Sladoje
Abstract:
Political scientists are increasingly interested in analyzing visual content at scale. However, the existing computational toolbox is still in need of methods and models attuned to the specific challenges and goals of social and political inquiry. In this article, we introduce a visual Structural Topic Model (vSTM) that combines pretrained image embeddings with a structural topic model. This has i…
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Political scientists are increasingly interested in analyzing visual content at scale. However, the existing computational toolbox is still in need of methods and models attuned to the specific challenges and goals of social and political inquiry. In this article, we introduce a visual Structural Topic Model (vSTM) that combines pretrained image embeddings with a structural topic model. This has important advantages compared to existing approaches. First, pretrained embeddings allow the model to capture the semantic complexity of images relevant to political contexts. Second, the structural topic model provides the ability to analyze how topics and covariates are related, while maintaining a nuanced representation of images as a mixture of multiple topics. In our empirical application, we show that the vSTM is able to identify topics that are interpretable, coherent, and substantively relevant to the study of online political communication.
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Submitted 14 April, 2025;
originally announced April 2025.
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Improved Visual Saliency of Graph Clusters with Orderable Node-Link Layouts
Authors:
Nora Al-Naami,
Nicolas Médoc,
Matteo Magnani,
Mohammad Ghoniem
Abstract:
Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can e…
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Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., `Linlog', `Backbone' and `sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.
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Submitted 21 August, 2024;
originally announced August 2024.
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On the accurate computation of expected modularity in probabilistic networks
Authors:
Xin Shen,
Matteo Magnani,
Christian Rohner,
Fiona Skerman
Abstract:
Modularity is one of the most widely used measures for evaluating communities in networks. In probabilistic networks, where the existence of edges is uncertain and uncertainty is represented by probabilities, the expected value of modularity can be used instead. However, efficiently computing expected modularity is challenging. To address this challenge, we propose a novel and efficient technique…
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Modularity is one of the most widely used measures for evaluating communities in networks. In probabilistic networks, where the existence of edges is uncertain and uncertainty is represented by probabilities, the expected value of modularity can be used instead. However, efficiently computing expected modularity is challenging. To address this challenge, we propose a novel and efficient technique (FPWP) for computing the probability distribution of modularity and its expected value. In this paper, we implement and compare our method and various general approaches for expected modularity computation in probabilistic networks. These include: (1) translating probabilistic networks into deterministic ones by removing low-probability edges or treating probabilities as weights, (2) using Monte Carlo sampling to approximate expected modularity, and (3) brute-force computation. We evaluate the accuracy and time efficiency of FPWP through comprehensive experiments on both real-world and synthetic networks with diverse characteristics. Our results demonstrate that removing low-probability edges or treating probabilities as weights produces inaccurate results, while the convergence of the sampling method varies with the parameters of the network. Brute-force computation, though accurate, is prohibitively slow. In contrast, our method is much faster than brute-force computation, but guarantees an accurate result.
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Submitted 22 April, 2025; v1 submitted 13 August, 2024;
originally announced August 2024.
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Modularity-based selection of the number of slices in temporal network clustering
Authors:
Patrik Seiron,
Axel Lindegren,
Matteo Magnani,
Christian Rohner,
Tsuyoshi Murata,
Petter Holme
Abstract:
A popular way to cluster a temporal network is to transform it into a sequence of networks, also called slices, where each slice corresponds to a time interval and contains the vertices and edges existing in that interval. A reason to perform this transformation is that after a network has been sliced, existing algorithms designed to find clusters in multilayer networks can be used. However, to us…
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A popular way to cluster a temporal network is to transform it into a sequence of networks, also called slices, where each slice corresponds to a time interval and contains the vertices and edges existing in that interval. A reason to perform this transformation is that after a network has been sliced, existing algorithms designed to find clusters in multilayer networks can be used. However, to use this approach, we need to know how many slices to generate. This chapter discusses how to select the number of slices when generalized modularity is used to identify the clusters.
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Submitted 24 November, 2023;
originally announced November 2023.
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Towards efficient multilayer network data management
Authors:
Georgios Panayiotou,
Matteo Magnani,
Bruno Pinaud
Abstract:
Real-world multilayer networks can be very large and there can be multiple choices regarding what should be modeled as a layer. Therefore, there is a need for their effective storage and manipulation. Currently, multilayer network analysis software use different data structures and manipulation operators. We aim to categorize operators in order to assess which structures work best for certain oper…
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Real-world multilayer networks can be very large and there can be multiple choices regarding what should be modeled as a layer. Therefore, there is a need for their effective storage and manipulation. Currently, multilayer network analysis software use different data structures and manipulation operators. We aim to categorize operators in order to assess which structures work best for certain operator classes and data features. In this work, we propose a preliminary taxonomy of layer and data manipulation operators. We also design and execute a benchmark of select software and operators to identify potential for optimization.
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Submitted 15 May, 2023;
originally announced May 2023.
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The Effectiveness of Embedded Values Analysis Modules in Computer Science Education: An Empirical Study
Authors:
Matthew Kopec,
Meica Magnani,
Vance Ricks,
Roben Torosyan,
John Basl,
Nicholas Miklaucic,
Felix Muzny,
Ronald Sandler,
Christo Wilson,
Adam Wisniewski-Jensen,
Cora Lundgren,
Kevin Mills,
Mark Wells
Abstract:
Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this…
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Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern's program that embeds values analysis modules into CS courses. The resulting data suggest that such modules have a positive effect on students' moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.
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Submitted 14 February, 2023; v1 submitted 10 August, 2022;
originally announced August 2022.
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Uncertainty in fMRI Functional Networks of Autism Brain Imaging Data
Authors:
Amin Kaveh,
Matteo Magnani,
Christian Rohner
Abstract:
In this paper we review the preprocessing pipeline through which fMRI data is transformed into a network. We discuss three parameters that mostly affect our understanding of the existence of functional correlations between the brain regions. In the end, we conclude that the existence of functional correlations between pairs of the brain's regions can be modeled with probabilistic edges, not to los…
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In this paper we review the preprocessing pipeline through which fMRI data is transformed into a network. We discuss three parameters that mostly affect our understanding of the existence of functional correlations between the brain regions. In the end, we conclude that the existence of functional correlations between pairs of the brain's regions can be modeled with probabilistic edges, not to lose the uncertainty that is inherent in the network generation process.
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Submitted 4 February, 2022;
originally announced February 2022.
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Multilayer network simplification: approaches, models and methods
Authors:
Roberto Interdonato,
Matteo Magnani,
Diego Perna,
Andrea Tagarelli,
Davide Vega
Abstract:
Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to an…
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Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or because traditional methods defined to analyze simple networks do not have a straightforward extension able to handle multiple layers. Therefore, a number of methods have been devised in the literature to simplify multilayer networks with the objective of improving our ability to analyze them. In this article we provide a unified and practical taxonomy of existing simplification approaches, and we identify categories of multilayer network simplification methods that are still underdeveloped, as well as emerging trends.
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Submitted 30 April, 2020;
originally announced April 2020.
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Community Detection in Multiplex Networks
Authors:
Matteo Magnani,
Obaida Hanteer,
Roberto Interdonato,
Luca Rossi,
Andrea Tagarelli
Abstract:
A multiplex network models different modes of interaction among same-type entities. In this article we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer t…
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A multiplex network models different modes of interaction among same-type entities. In this article we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.
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Submitted 20 January, 2021; v1 submitted 16 October, 2019;
originally announced October 2019.
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An Analysis of the Consequences of the General Data Protection Regulation (GDPR) on Social Network Research
Authors:
Andreas Kotsios,
Matteo Magnani,
Luca Rossi,
Irina Shklovski,
Davide Vega
Abstract:
This article examines the principles outlined in the General Data Protection Regulation (GDPR) in the context of social network data. We provide both a practical guide to GDPR-compliant social network data processing, covering aspects such as data collection, consent, anonymization and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulat…
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This article examines the principles outlined in the General Data Protection Regulation (GDPR) in the context of social network data. We provide both a practical guide to GDPR-compliant social network data processing, covering aspects such as data collection, consent, anonymization and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulation is based are instantiated to this research area.
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Submitted 5 October, 2019; v1 submitted 7 March, 2019;
originally announced March 2019.
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Foundations of Temporal Text Networks
Authors:
Davide Vega,
Matteo Magnani
Abstract:
Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. T…
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Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.
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Submitted 23 June, 2018; v1 submitted 7 March, 2018;
originally announced March 2018.
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Quantifying layer similarity in multiplex networks: a systematic study
Authors:
Piotr Bródka,
Anna Chmiel,
Matteo Magnani,
Giancarlo Ragozini
Abstract:
Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practica…
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Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.
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Submitted 30 November, 2017;
originally announced November 2017.
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On Joining Graphs
Authors:
Giacomo Bergami,
Matteo Magnani,
Danilo Montesi
Abstract:
In the graph database literature the term "join" does not refer to an operator used to merge two graphs. In particular, a counterpart of the relational join is not present in existing graph query languages, and consequently no efficient algorithms have been developed for this operator.
This paper provides two main contributions. First, we define a binary graph join operator that acts on the vert…
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In the graph database literature the term "join" does not refer to an operator used to merge two graphs. In particular, a counterpart of the relational join is not present in existing graph query languages, and consequently no efficient algorithms have been developed for this operator.
This paper provides two main contributions. First, we define a binary graph join operator that acts on the vertices as a standard relational join and combines the edges according to a user-defined semantics. Then we propose the "CoGrouped Graph Conjunctive $θ$-Join" algorithm running over data indexed in secondary memory. Our implementation outperforms the execution of the same operation in Cypher and SPARQL on major existing graph database management systems by at least one order of magnitude, also including indexing and loading time.
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Submitted 19 August, 2016;
originally announced August 2016.
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A simple multiforce layout for multiplex networks
Authors:
Zahra Fatemi,
Mostafa Salehi,
Matteo Magnani
Abstract:
We introduce multiforce, a force-directed layout for multiplex networks, where the nodes of the network are organized into multiple layers and both in-layer and inter-layer relationships among nodes are used to compute node coordinates. The proposed approach generalizes existing work, providing a range of intermediate layouts in-between the ones produced by known methods. Our experiments on real d…
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We introduce multiforce, a force-directed layout for multiplex networks, where the nodes of the network are organized into multiple layers and both in-layer and inter-layer relationships among nodes are used to compute node coordinates. The proposed approach generalizes existing work, providing a range of intermediate layouts in-between the ones produced by known methods. Our experiments on real data show that multiforce can keep nodes well aligned across different layers without significantly affecting their internal layouts when the layers have similar or compatible topologies. As a consequence, multiforce enriches the benefits of force-directed layouts by also supporting the identification of topological correspondences between layers.
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Submitted 30 December, 2016; v1 submitted 13 July, 2016;
originally announced July 2016.
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Finding overlapping communities in multiplex networks
Authors:
Nazanin Afsarmanesh,
Matteo Magnani
Abstract:
We define an approach to identify overlapping communities in multiplex networks, extending the popular clique percolation method for simple graphs. The extension requires to rethink the basic concepts on which the clique percolation algorithm is based, including cliques and clique adjacency, to allow the presence of multiple types of edges.
We define an approach to identify overlapping communities in multiplex networks, extending the popular clique percolation method for simple graphs. The extension requires to rethink the basic concepts on which the clique percolation algorithm is based, including cliques and clique adjacency, to allow the presence of multiple types of edges.
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Submitted 7 March, 2016; v1 submitted 11 February, 2016;
originally announced February 2016.
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Clustering attributed graphs: models, measures and methods
Authors:
Cecile Bothorel,
Juan David Cruz,
Matteo Magnani,
Barbora Micenkova
Abstract:
Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are t…
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Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.
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Submitted 7 January, 2015;
originally announced January 2015.
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Towards effective visual analytics on multiplex and multilayer networks
Authors:
Matteo Magnani,
Luca Rossi
Abstract:
In this article we discuss visualisation strategies for multiplex networks. Since Moreno's early works on network analysis, visualisation has been one of the main ways to understand networks thanks to its ability to summarise a complex structure into a single representation highlighting multiple properties of the data. However, despite the large renewed interest in the analysis of multiplex networ…
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In this article we discuss visualisation strategies for multiplex networks. Since Moreno's early works on network analysis, visualisation has been one of the main ways to understand networks thanks to its ability to summarise a complex structure into a single representation highlighting multiple properties of the data. However, despite the large renewed interest in the analysis of multiplex networks, no study has proposed specialised visualisation approaches for this context and traditional methods are typically applied instead. In this paper we initiate a critical and structured discussion of this topic, and claim that the development of specific visualisation methods for multiplex networks will be one of the main drivers pushing current research results into daily practice.
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Submitted 7 January, 2015;
originally announced January 2015.
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Multidimensional epidemic thresholds in diffusion processes over interdependent networks
Authors:
Mostafa Salehi,
Payam Siyari,
Matteo Magnani,
Danilo Montesi
Abstract:
Several systems can be modeled as sets of interdependent networks where each network contains distinct nodes. Diffusion processes like the spreading of a disease or the propagation of information constitute fundamental phenomena occurring over such coupled networks. In this paper we propose a new concept of multidimensional epidemic threshold characterizing diffusion processes over interdependent…
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Several systems can be modeled as sets of interdependent networks where each network contains distinct nodes. Diffusion processes like the spreading of a disease or the propagation of information constitute fundamental phenomena occurring over such coupled networks. In this paper we propose a new concept of multidimensional epidemic threshold characterizing diffusion processes over interdependent networks, allowing different diffusion rates on the different networks and arbitrary degree distributions. We analytically derive and numerically illustrate the conditions for multilayer epidemics, i.e., the appearance of a giant connected component spanning all the networks. Furthermore, we study the evolution of infection density and diffusion dynamics with extensive simulation experiments on synthetic and real networks.
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Submitted 26 December, 2014;
originally announced December 2014.
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Missing data in multiplex networks: a preliminary study
Authors:
Rajesh Sharma,
Matteo Magnani,
Danilo Montesi
Abstract:
A basic problem in the analysis of social networks is missing data. When a network model does not accurately capture all the actors or relationships in the social system under study, measures computed on the network and ultimately the final outcomes of the analysis can be severely distorted. For this reason, researchers in social network analysis have characterised the impact of different types of…
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A basic problem in the analysis of social networks is missing data. When a network model does not accurately capture all the actors or relationships in the social system under study, measures computed on the network and ultimately the final outcomes of the analysis can be severely distorted. For this reason, researchers in social network analysis have characterised the impact of different types of missing data on existing network measures. Recently a lot of attention has been devoted to the study of multiple-network systems, e.g., multiplex networks. In these systems missing data has an even more significant impact on the outcomes of the analyses. However, to the best of our knowledge, no study has focused on this problem yet. This work is a first step in the direction of understanding the impact of missing data in multiple networks. We first discuss the main reasons for missingness in these systems, then we explore the relation between various types of missing information and their effect on network properties. We provide initial experimental evidence based on both real and synthetic data.
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Submitted 26 September, 2014;
originally announced September 2014.
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Diffusion of Innovations over Multiplex Social Networks
Authors:
Rasoul Ramezanian,
Mostafa Salehi,
Matteo Magnani,
Danilo Montesi
Abstract:
The ways in which an innovation (e.g., new behaviour, idea, technology, product) diffuses among people can determine its success or failure. In this paper, we address the problem of diffusion of innovations over multiplex social networks where the neighbours of a person belong to one or multiple networks (or layers) such as friends, families, or colleagues. To this end, we generalise one of the ba…
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The ways in which an innovation (e.g., new behaviour, idea, technology, product) diffuses among people can determine its success or failure. In this paper, we address the problem of diffusion of innovations over multiplex social networks where the neighbours of a person belong to one or multiple networks (or layers) such as friends, families, or colleagues. To this end, we generalise one of the basic game-theoretic diffusion models, called networked coordination game, for multiplex networks. We present analytical results for this extended model and validate them through a simulation study, finding among other properties a lower bound for the success of an innovation.While simple and leading to intuitively understandable results, to the best of our knowledge this is the first extension of a game-theoretic innovation diffusion model for multiplex networks and as such it provides a basic framework to study more sophisticated innovation dynamics.
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Submitted 25 August, 2014;
originally announced August 2014.
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Spreading processes in Multilayer Networks
Authors:
Mostafa Salehi,
Rajesh Sharma,
Moreno Marzolla,
Matteo Magnani,
Payam Siyari,
Danilo Montesi
Abstract:
Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while…
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Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.
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Submitted 4 December, 2014; v1 submitted 16 May, 2014;
originally announced May 2014.
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Combinatorial Analysis of Multiple Networks
Authors:
Matteo Magnani,
Barbora Micenkova,
Luca Rossi
Abstract:
The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the development of multi-layer networks. These models have the potential of becoming the reference tools in network data analysis, but require the parallel development of spe…
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The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the development of multi-layer networks. These models have the potential of becoming the reference tools in network data analysis, but require the parallel development of specific analysis methods explicitly exploiting the information hidden in-between the layers and the availability of a critical mass of reference data to experiment with the tools and investigate the real-world organization of these complex systems. In this work we introduce a real-world layered network combining different kinds of online and offline relationships, and present an innovative methodology and related analysis tools suggesting the existence of hidden motifs traversing and correlating different representation layers. We also introduce a notion of betweenness centrality for multiple networks. While some preliminary experimental evidence is reported, our hypotheses are still largely unverified, and in our opinion this calls for the availability of new analysis methods but also new reference multi-layer social network data.
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Submitted 20 March, 2013;
originally announced March 2013.
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Joining relations under discrete uncertainty
Authors:
Matteo Magnani,
Danilo Montesi
Abstract:
In this paper we introduce and experimentally compare alternative algorithms to join uncertain relations. Different algorithms are based on specific principles, e.g., sorting, indexing, or building intermediate relational tables to apply traditional approaches. As a consequence their performance is affected by different features of the input data, and each algorithm is shown to be more efficient t…
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In this paper we introduce and experimentally compare alternative algorithms to join uncertain relations. Different algorithms are based on specific principles, e.g., sorting, indexing, or building intermediate relational tables to apply traditional approaches. As a consequence their performance is affected by different features of the input data, and each algorithm is shown to be more efficient than the others in specific cases. In this way statistics explicitly representing the amount and kind of uncertainty in the input uncertain relations can be used to choose the most efficient algorithm.
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Submitted 1 November, 2012;
originally announced November 2012.
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Multi-Stratum Networks: toward a unified model of on-line identities
Authors:
Matteo Magnani,
Luca Rossi
Abstract:
One of the reasons behind the success of Social Network Analysis is its simple and general graph model made of nodes (representing individuals) and ties. However, when we focus on our daily on-line experience we must confront a more complex scenario: people inhabitate several on-line spaces interacting to several communities active on various technological infrastructures like Twitter, Facebook, Y…
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One of the reasons behind the success of Social Network Analysis is its simple and general graph model made of nodes (representing individuals) and ties. However, when we focus on our daily on-line experience we must confront a more complex scenario: people inhabitate several on-line spaces interacting to several communities active on various technological infrastructures like Twitter, Facebook, YouTube or FourSquare and with distinct social objectives. This constitutes a complex network of interconnected networks where users' identities are spread and where information propagates navigating through different communities and social platforms. In this article we introduce a model for this layered scenario that we call multi-stratum network. Through a theoretical discussion and the analysis of real-world data we show how not only focusing on a single network may provide a very partial understanding of the role of its users, but also that considering all the networks separately may not reveal the information contained in the whole multi-stratum model.
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Submitted 1 November, 2012;
originally announced November 2012.
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BPDMN: A Conservative Extension of BPMN with Enhanced Data Representation Capabilities
Authors:
Matteo Magnani,
Danilo Montesi
Abstract:
The design of business processes involves the usage of modeling languages, tools and methodologies. In this paper we highlight and address a relevant limitation of the Business Process Modeling Notation (BPMN): its weak data representation capabilities. In particular, we extend it with data-specific constructs derived from existing data modeling notations and adapted to blend gracefully into BPM…
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The design of business processes involves the usage of modeling languages, tools and methodologies. In this paper we highlight and address a relevant limitation of the Business Process Modeling Notation (BPMN): its weak data representation capabilities. In particular, we extend it with data-specific constructs derived from existing data modeling notations and adapted to blend gracefully into BPMN diagrams. The extension has been developed taking existing modeling languages and requirement analyses into account: we characterize our notation using the Workfl ow Data Patterns and provide mappings to the main XML-based business process languages.
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Submitted 11 July, 2009;
originally announced July 2009.
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ChOrDa: a methodology for the modeling of business processes with BPMN
Authors:
Matteo Buferli,
Matteo Magnani,
Danilo Montesi
Abstract:
In this paper we present a modeling methodology for BPMN, the standard notation for the representation of business processes. Our methodology simplifies the development of collaborative BPMN diagrams, enabling the automated creation of skeleton process diagrams representing complex choreographies. To evaluate and tune the methodology, we have developed a tool supporting it, that we apply to the…
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In this paper we present a modeling methodology for BPMN, the standard notation for the representation of business processes. Our methodology simplifies the development of collaborative BPMN diagrams, enabling the automated creation of skeleton process diagrams representing complex choreographies. To evaluate and tune the methodology, we have developed a tool supporting it, that we apply to the modeling of an international patenting process as a working example.
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Submitted 7 July, 2009;
originally announced July 2009.