arXiv subjects

Vincent Labatut

Publications and source records attributed to Vincent Labatut.

At least 55 records · Page 3Linked to original sources

arXiv2018

Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry.

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arXiv2018

Signed Graph Analysis for the Interpretation of Voting Behavior

In a signed graph, each link is labeled with either a positive or a negative sign. This is particularly appropriate to model polarized systems. Such a graph can be characterized through the notion of structural balance, which relies on the partitioning of the graph into internally solidary but mutually hostile subgroups. In this work, we show that signed graphs can be used to model and understand voting behavior. We take advantage of data from the European Parliament to confront two variants of structural balance, and illustrate how their use can help better understanding the studied system.

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arXiv2017

Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach.

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arXiv2017

Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach

Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition.

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arXiv2017

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

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arXiv2017

Impact Of Content Features For Automatic Online Abuse Detection

Online communities have gained considerable importance in recent years due to the increasing number of people connected to the Internet. Moderating user content in online communities is mainly performed manually, and reducing the workload through automatic methods is of great financial interest for community maintainers. Often, the industry uses basic approaches such as bad words filtering and regular expression matching to assist the moderators. In this article, we consider the task of automatically determining if a message is abusive. This task is complex since messages are written in a non-standardized way, including spelling errors, abbreviations, community-specific codes... First, we evaluate the system that we propose using standard features of online messages. Then, we evaluate the impact of the addition of pre-processing strategies, as well as original specific features developed for the community of an online in-browser strategy game. We finally propose to analyze the usefulness of this wide range of features using feature selection. This work can lead to two possible applications: 1) automatically flag potentially abusive messages to draw the moderator's attention on a narrow subset of messages ; and 2) fully automate the moderation process by deciding whether a message is abusive without any human intervention.

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arXiv2016

Straightness of rectilinear vs. radio-concentric networks: modeling simulation and comparison

This paper proposes a comparison between rectilinear and radio-concentric networks. Indeed, those networks are often observed in urban areas, in several cities all over the world. One of the interesting properties of such networks is described by the \textit{straightness} measure from graph theory, which assesses how much moving from one node to another along the network links departs from the network-independent straightforward path. We study this property in both rectilinear and radio-concentric networks, first by analyzing mathematically routes from the center to peripheral locations in a theoretical framework with perfect topology, then using simulations for multiple origin-destination paths. We show that in most of the cases, radio-concentric networks have a better straightness than rectilinear ones. How may this property be used in the future for urban networks?

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arXiv2016

A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence

Many works related to Twitter aim at characterizing its users in some way: role on the service (spammers, bots, organizations, etc.), nature of the user (socio-professional category, age, etc.), topics of interest , and others. However, for a given user classification problem, it is very difficult to select a set of appropriate features, because the many features described in the literature are very heterogeneous, with name overlaps and collisions, and numerous very close variants. In this article, we review a wide range of such features. In order to present a clear state-of-the-art description, we unify their names, definitions and relationships, and we propose a new, neutral, typology. We then illustrate the interest of our review by applying a selection of these features to the offline influence detection problem. This task consists in identifying users which are influential in real-life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, We propose several content-based approaches to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.

cs.CL↗

arXiv2016

Generalized Measures for the Evaluation of Community Detection Methods

Community detection can be considered as a variant of cluster analysis applied to complex networks. For this reason, all existing studies have been using tools derived from this field when evaluating community detection algorithms. However, those are not completely relevant in the context of network analysis, because they ignore an essential part of the available information: the network structure. Therefore, they can lead to incorrect interpretations. In this article, we review these measures, and illustrate this limitation. We propose a modification to solve this problem, and apply it to the three most widespread measures: purity, Rand index and normalized mutual information (NMI). We then perform an experimental evaluation on artificially generated networks with realistic community structure. We assess the relevance of the modified measures by comparison with their traditional counterparts, and also relatively to the topological properties of the community structures. On these data, the modified NMI turns out to provide the most relevant results.

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arXiv2015

Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament

In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the VoteWatch Europe Website for the whole 7 th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.

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arXiv2015

Interpreting communities based on the evolution of a dynamic attributed network

Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle, or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.

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arXiv2015

A community role approach to assess social capitalists visibility in the Twitter network

In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understanding their position in Twit-ter is thus of important interest. It is also necessary to analyze how these methods effectively affect user visibility. Based on a recently proposed method allowing to identify social capitalists, we tackle both points by studying how they are organized, and how their links spread across the Twitter follower-followee network. To that aim, we consider their position in the network w.r.t. its community structure. We use the concept of community role of a node, which describes its position in a network depending on its connectiv-ity at the community level. However, the topological measures originally defined to characterize these roles consider only certain aspects of the community-related connectivity, and rely on a set of empirically fixed thresholds. We first show the limitations of these measures, before extending and generalizing them. Moreover, we use an unsupervised approach to identify the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show they are highly visible on Twitter, due to the specific roles they hold.

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arXiv2015

Étude de l'omniprésence des propriétés petit- monde et sans-échelle

The small-world and scale-free properties were identified in real-world complex net-works at the end of the 90s. Their analysis led to a better understanding of the dynamics and functioning of certain systems, and they were studied in many subsequent works. This might be the reason why one frequently finds, in the complex networks literature, assertions regarding their ubiquity, their validity for almost all complex networks. Yet, the mentioned seminal works were conducted on a very limited number of networks, and, to the best of our knowledge, no large-scale study has been conducted to answer this question. In this work, we take advantage, on the one hand, of the many datasets now available online, to constitute a large collection of networks, and on the other hand, of recent analysis tools, to check the validity of this hypothesis of ubiquity. It turns out a large majority of the studied networks are indeed small-world, how-ever this is not the case for the scale-free property, since the degree distribution of a significant proportion of our networks does not follow a power-law. MOTS-CL{É}S : R{é}seaux complexes, propri{é}t{é}s topologiques, petit-monde, sans-{é}chelle.

math.HO↗

arXiv2014

Identifying the Community Roles of Social Capitalists in the Twitter Network

In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the Twitter network since they flaw notions of influence and visibility. Indeed, it has recently been observed that they are real and active users that can help malicious users such as spammers gaining influence. Studying their behavior and understanding their position in Twitter is thus of important interest. A recent work provided an efficient way to detect social capitalists using two simple topological measures. Based on this detection method, we study how social capitalists are distributed over Twitter's friend-to-follower network. We are especially interested in analyzing how they are organized, and how their links spread across the network. Answering these questions allows to know whether the social capitalism methods increase the actual visibility on the service. To that aim, we study the position of social capitalists on Twitter w.r.t. the community structure of the network. We base our work on the concept of community role of a node, which describes its position in a network depending on its connectivity at the community level. The topological measures originally defined to characterize these roles consider only some aspects of community-related connectivity and rely on a set of empirically fixed thresholds. We first show the limitations of such measures and then extend and generalize them by considering new aspects of the community-related connectivity. Moreover, we use an unsupervised approach to distinguish the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show that they are highly visible on Twitter, due to the specific roles they occupy.

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arXiv2014

A Method for Characterizing Communities in Dynamic Attributed Complex Networks

Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. In its simplest form, a community structure takes the form of a partition of the node set. From the modeling point of view, to be of some utility, this partition must then be characterized relatively to the properties of the studied system. However, if most of the existing works focus on defining methods for the detection of communities, only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either in the type of data they handle, or by the nature of the results they output. In this work, we propose a method to efficiently support such a characterization task. We first define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We also show how to detect unusual behavior in a community, and highlight outliers. Finally, as an illustration, we apply our method to a network of scientific collaborations.

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arXiv2014

Classification of Complex Networks Based on Topological Properties

Complex networks are a powerful modeling tool, allowing the study of countless real-world systems. They have been used in very different domains such as computer science, biology, sociology, management, etc. Authors have been trying to characterize them using various measures such as degree distribution, transitivity or average distance. Their goal is to detect certain properties such as the small-world or scale-free properties. Previous works have shown some of these properties are present in many different systems, while others are characteristic of certain types of systems only. However, each one of these studies generally focuses on a very small number of topological measures and networks. In this work, we aim at using a more systematic approach. We first constitute a dataset of 152 publicly available networks, spanning over 7 different domains. We then process 14 different topological measures to characterize them in the most possible complete way. Finally, we apply standard data mining tools to analyze these data. A cluster analysis reveals it is possible to obtain two significantly distinct clusters of networks, corresponding roughly to a bisection of the domains modeled by the networks. On these data, the most discriminant measures are density, modularity, average degree and transitivity, and at a lesser extent, closeness and edgebetweenness centralities.Abstract--Complex networks are a powerful modeling tool, allowing the study of countless real-world systems. They have been used in very different domains such as computer science, biology, sociology, management, etc. Authors have been trying to characterize them using various measures such as degree distribution, transitivity or average distance. Their goal is to detect certain properties such as the small-world or scale-free properties. Previous works have shown some of these properties are present in many different systems, while others are characteristic of certain types of systems only. However, each one of these studies generally focuses on a very small number of topological measures and networks. In this work, we aim at using a more systematic approach. We first constitute a dataset of 152 publicly available networks, spanning over 7 different domains. We then process 14 different topological measures to characterize them in the most possible complete way. Finally, we apply standard data mining tools to analyze these data. A cluster analysis reveals it is possible to obtain two significantly distinct clusters of networks, corresponding roughly to a bisection of the domains modeled by the networks. On these data, the most discriminant measures are density, modularity, average degree and transitivity, and at a lesser extent, closeness and edgebetweenness centralities.

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arXiv2013

Une méthode pour caractériser les communautés des réseaux dynamiques à attributs

Many complex systems are modeled through complex networks whose analysis reveals typical topological properties. Amongst those, the community structure is one of the most studied. Many methods are proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic networks. A community structure takes the form of a partition of the node set, which must then be characterized relatively to the properties of the studied system. We propose a method to support such a characterization task. We define a sequence-based representation of networks, combining temporal information, topological measures, and nodal attributes. We then characterize communities using the most representative emerging sequential patterns of its nodes. This also allows detecting unusual behavior in a community. We describe an empirical study of a network of scientific collaborations.---De nombreux systèmes complexes sont étudiés via l'analyse de réseaux dits complexes ayant des propriétés topologiques typiques. Parmi cellesci, les structures de communautés sont particulièrement étudiées. De nombreuses méthodes permettent de les détecter, y compris dans des réseaux contenant des attributs nodaux, des liens orientés ou évoluant dans le temps. La détection prend la forme d'une partition de l'ensemble des noeuds, qu'il faut ensuite caractériser relativement au système modélisé. Nous travaillons sur l'assistance à cette tâche de caractérisation. Nous proposons une représentation des réseaux sous la forme de séquences de descripteurs de noeuds, qui combinent les informations temporelles, les mesures topologiques, et les valeurs des attributs nodaux. Les communautés sont caractérisées au moyen des motifs séquentiels émergents les plus représentatifs issus de leurs noeuds. Ceci permet notamment la détection de comportements inhabituels au sein d'une communauté. Nous décrivons une étude empirique sur un réseau de collaboration scientifique.

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arXiv2013

Identification de rôles communautaires dans des réseaux orientés appliquée à Twitter

The notion of community structure is particularly useful when analyzing complex networks, because it provides an intermediate level, compared to the more classic global (whole network) and local (node neighborhood) approaches. The concept of community role of a node was derived from this base, in order to describe the position of a node in a network depending on its connectivity at the community level. However, the existing approaches are restricted to undirected networks, use topological measures which do not consider all aspects of community-related connectivity, and their role identification methods are not generalizable to all networks. We tackle these limitations by generalizing and extending the measures, and using an unsupervised approach to determine the roles. We then illustrate the applicability of our method by analyzing a Twitter network.We show how our modifications allow discovering the fact some particular users called social capitalists occupy very specific roles in this system. --- La notion de structure de communautés est particulièrement utile pour étudier les réseaux complexes, car elle amène un niveau d'analyse intermédiaire, par opposition aux plus classiques niveaux local (voisinage des noeuds) et global (réseau entier). Le concept de rôle communautaire permet de décrire le positionnement d'un noeud en fonction de sa connectivité communautaire. Cependant, les approches existantes sont restreintes aux réseaux non-orientés, utilisent des mesures topologiques ne considérant pas tous les aspects de la connectivité communautaire, et des méthodes d'identification des rôles non-généralisables à tous les réseaux. Nous proposons de résoudre ces problèmes en généralisant les mesures existantes, et en utilisant une méthode non-supervisée pour déterminer les rôles. Nous illustrons l'intérêt de notre méthode en l'appliquant au réseau de Twitter. Nous montrons que nos modifications mettent en évidence les rôles spécifiques d'utilisateurs particuliers du réseau, nommés capitalistes sociaux.

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