Ayan Kumar Bhomwick

Wednesday 19th 2018

Slides

No abstract available.

Prof. Dr. Natasa Przulj

December 7th 2018, 14:00. Salle 24-25/405, LIP6 – UMPC, Sorbonne Université. 4 Place Jussieu, 75005 Paris.

We are faced with a flood of molecular and clinical data. Various bio-molecules interact in a cell to perform biological function, forming large, complex systems. Large-scale patient-specific omics datasets are increasingly becoming available, providing heterogeneous, but complementary information about cells, tissues and diseases. The challenge is how to mine these interacting, complex, complementary data systems to answer fundamental biological and medical questions.  Dealing with them is nontrivial, because many questions we ask to answer from them fall into the category of computationally intractable problems, necessitating the development of heuristic methods for finding approximate solutions. We develop methods for extracting new biomedical knowledge from the wiring patterns of systems-level, heterogeneous, networked biomedical data.  Our methods link the patterns in molecular networks and the multi-scale network organization with biological function.  In this way, we translate the information hidden in the wiring patterns into domain-specific knowledge. In addition, we introduce a versatile data fusion (integration) framework that can effectively integrate the information obtained from mining molecular networks with patient-specific somatic mutation data and drug chemical data to address key challenges in precision medicine: better stratification of patients, prediction of driver genes in cancer, and re-purposing of approved drugs to particular patients and patient groups. Our new methods stem from novel network science approaches coupled with graph-regularized non-negative matrix tri-factorization, a machine learning technique for dimensionality reduction and co-clustering of heterogeneous datasets. We utilize our new framework to develop methodologies for performing other related tasks, including disease re-classification from modern, heterogeneous molecular level data, inferring new Gene Ontology relationships, and aligning multiple molecular networks.

Lionel Tabourier

September 24th 2018, 11am, room 25-26/105, Jussieu

Slides

Contacts entre individus, interactions sociales, transactions économiques ou encore machines échangeant des paquets d’information, tous ces systèmes ont en commun d’être constitués d’éléments en interaction et dépourvus de coordination par un « cerveau central ». Par conséquent, la structure de leurs interactions résultent de processus décentralisés, qui sont souvent mal connus. Depuis les années 90, il a été mis en évidence que la représentation en graphe de tels systèmes amenait à la découverte de propriétés communes, cela a permis l’utilisation de méthodes transverses pour les décrire et en comprendre les mécanismes sous-jacents. Ces études ont ensuite évolué pour constituer un champ de recherche à part entière : l’analyse de réseaux complexes. Parce qu’elles sont simples et qu’il existe un important volume de connaissance en théorie et en algorithmique de graphes, les représentations en graphes de tels systèmes en interaction ont mené à d’importants succès. Cependant, l’accès généralisé à des jeux de données en ligne a également mis en évidence la nécessité de prendre en compte l’aspect fondamentalement dynamique des données d’interaction. Mon travail de recherche touche à plusieurs aspects de l’évolution d’une représentation statique à une représentation dynamique de telles données. Celui-ci est organisé en trois axes distincts : le premier concerne la description de processus dynamiques sur des réseaux évoluant dans le temps, et plus précisément les phénomènes de diffusion. Le second axe se rapporte au problème de la prévision d’interactions dans un réseau temporel. Enfin, le troisième s’interroge sur la modélisation de la structure des interactions au moyen de réseaux aléatoires qui imitent la structure des données réelles.

Marco Fiore

Monday, September 24th 2018,  4pm, room 24-25/405

Slides

Mobile networks provide support to a variety of communication-based services that are steadily changing our lives. However, they are also pervasive infrastructures that can be used in unconventional ways unrelated to communication. Specifically, mobile networks can be seen as large-scale remote sensing platform capable of providing fine-grained information about a large (and increasing) fraction of the worldwide population. In this talk, I will discuss two case studies of remote sensing based on mobile networks: land use mapmaking, i.e., the detection of arrangements and activities in a target geographical region, and population density estimation, i.e., the monitoring of dwelling units and people presence. Solutions to both these problems have important applications in, e.g., urban planning and transportations, and can benefit from approaches that take advantage of the mobile network infrastructure.

Patrick Doreian

Friday, June 22th 2018, 14h, salle 26-00/101

The nature of the Supreme Court, its decisions, the principle of Stare Decisis are described. This is followed by a listing of the Supreme Court networks that are considered. This includes: the citation network of decisions citing earlier decisions (1789-2001); coherent clusters of decisions obtained by examining the extent to which they are co-cited by later decisions; signed two-mode networks featuring Justices and their votes for annual terms of the Court; one-mode signed networks of justices with the extent to which they vote with or against each other; and signed networks of Courts overturning decisions of prior Courts or their own decisions. Strategies for analyzing these networks are presented along with the subsequent results. Throughout, the network results are linked to substantive topics and constitutional principles. A long-term research agenda is presented.

Vincent Cohen-Addad

Wednesday, May 16th, 2018, 11h, salle 26-00/101, Campus Jussieu

Slides

Hiererachical clustering, that is computing a recursive partitioning of a dataset to obtain clusters at increasingly finer granularity is a fundamental problem in data analysis. Although hierarchical clustering has mostly been studied through procedures such as linkage algorithms, or top-down heuristics, rather than as optimization problems, Dasgupta recently proposed an objective function for hierarchical clustering and initiated a line of work developing algorithms that explicitly optimize an objective. In this paper, we consider a fairly general random graph model for hierarchical clustering, called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry combined with specific linkage methods results in a clustering that give an O(1) approximation to Dasguptas cost function. Finally, we report empirical evaluation on synthetic and real-world data showing that our proposed SVD-based method does indeed achieve a better cost than other widely-used heurstics and also results in a better classification accuracy when the underlying problem was that of multi-class classification.

Shweta Jain

Thursday, May 3th, 2018, 11h, salle 26-00/101, Campus Jussieu

Slides

Large graphs have become commonplace making many of the traditional graph-theoretic algorithms infeasible. Moreover, sometimes we don’t have access to the whole graph. This has led us to revisit graph algorithms and necessitated graph sampling. In this talk, I will explore 2 applications of graph sampling. In the first application, called TurnShadow, we propose a method for efficient counting of k-cliques. It uses the classic result called Turn’s theorem to give provable error bounds. We also do extensive evaluation of the method on real-world graph instances and demonstrate that it is fast and extremely accurate. In the second application, we propose a method called SADDLES to estimate the degree distribution when we are given only limited access to the graph, and accessing the graph is costly. We assume we have access to uniform at random (u.a.r ) vertex, u.a.r neighbor of a vertex and the degree of the vertex. We compare SADDLES with many other state-of-the-art methods and demonstrate that SADDLES requires far fewer samples to achieve the same degree of accuracy.    

Di Niu

Friday, May 4th, 2018, 11h, salle 26-00/428, Campus Jussieu

Slides

I will describe our recent experience of implementing a news content organization system in collaboration with Tencent that can discover hot events from vast streams of breaking news and connect events into stories for easy viewing. Our real-world system has distinct requirements in contrast to previous studies on document topic modeling and detection, in that 1) an event does not only contain articles of a similar topic, but is a cluster of documents that report exactly the same physical incidence; 2) we must evolve news stories in a logical and online manner. In solving these challenges, we propose Story Forest, a state-of-the-art news content organization system based on artificial intelligence and natural language processing. I will briefly describe the key enabling technologies in Story Forest, including identifying the relationship between text objects, e.g., whether they talk about the same event or whether one article is a follow-up of another, based on deep learning. Our system has been deployed in Tencent QQ Browser mobile app.

Sylvain Castagnos

Thursday, May 17th, 2018, 11h, salle 26-00/101, Campus Jussieu

Slides

Recent studies have highlighted the correlation between users’ satisfaction and diversity within recommenders, especially the fact that diversity increases users’ confidence when choosing an item. Understanding the reasons of this positive impact on recommenders is now becoming crucial. Based on this assumption, we designed a user study that focuses on the utility of this new dimension, as well as its perceived qualities. This study has been conducted on 250 users and it compared 5 recommendation approaches, based on collaborative filtering, content-based filtering and popularity, along with various degrees of diversity. Results show that, when recommendations are made explicit, diversity may reduce users’ acceptance rate. However, it helps increasing users’ satisfaction. Moreover, this study highlights the need to build users’ preference models that are diverse enough, so as to generate good recommendations.

Henrik Palmer Olsen

Jeudi 16 Novembre 2017, 14h, salle 26-00/332, Campus Jussieu

This presentation explores how the complete collected set of judgments from the European Court of Human Rights can be presented as a network of case to references, and how this network can be analysed via the use of already known network analysis approaches. The presentation will first show how network analysis can be used to calculate the main legal content of a case (i.e. which legal right the case is mostly concerned with and for which the case is therefore a precedent). It will then move on to showing how, Page-rank of a case in the total network of cases can be compared to the Page-rank of the same case in its local network and how this comparison can be used to further calculate generic centrality in the overall network. In using this novel approach, it is possible to identify cases that have strong precedent, not for a specific right, but for more generic legal content that the Court uses in dealing with applications from citizens.

David Ellison

Jeudi 15 Juin 2017 à 11h, Salle 24-25/405, Campus Jussieu

Le jeu du gendarme et du voleur, introduit par Alain Quilliot dans sa thèse en 1978, est un jeu à deux joueurs sur un graphe. Le gendarme commence en choisissant son point de départ sur un sommet du graphe ; puis le voleur choisit le sien. Ensuite, ils se déplacent chacun leur tour le long des arêtes du graphe. La question est de savoir si le gendarme a une stratégie qui lui permet d’attraper le voleur. Dans le cas contraire, la question devient : combien faut-il de gendarmes pour attraper le voleur ? Quilliot a démontré dans sa thèse qu’un seul gendarme suffit à attraper le voleur si et seulement si le graphe est démontable, c’est-à-dire si et seulement si on peut le réduire à un seul sommet en retirant successivement des sommets où le voleur peut être coincé. Il s’ensuit que les graphes démontables correspondent à la classe d’homotopie du point, et que certains invariants homotopiques, comme les groupes d’homologie, permettent de découvrir des propriétés structurelles des graphes où le gendarme peut attraper le voleur.

Christine Largeron

Mardi 25 Avril 2017 à 11h, Salle 24-25/405, Campus Jussieu

A wide variety of applications require data modeled by a graph of interconnected nodes, known as social networks or information networks. When nodes are described by attributes, this is an attributed network.Examples of such networks are citation or collaboration networks or social media. One fundamental property of these networks is that they tend to naturally form communities and, many mining methods have been proposed to identify these communities but these methods are typically basedsolely on relationships between nodes in the graph. This is notably the case of Louvain, a greedy agglomerative clustering method which optimizes the modularity measure. Introduced by Newman, the modularity allows to estimate the quality of a partition butwithout taking into account the attributes associated with the nodes. For this reason, we have designed a complementary measure, based on inertia and specially conceived to evaluate the quality of a partition basedon real attributes describing the vertices. We have also proposed I-Louvain, a method for community detection in attributed graphs where real attributes are associated with the vertices. Our experiments showedthat combining the relational information with the attributes allows to detect the communities more efficiently with poor data.

Federico Battiston

11 Avril 2017, 11h, Salle 24-25/405

Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes, rather than in terms of (single-layer) networks. In this talk I will introduce multiplex networks and several metrics to measure multiplexity at different scales. Measures are validated and applied to real-world systems with examples from collaboration networks, terrorist networks and the brain. I will also show how multiplexity can produce the emergence of qualitatively novel dynamical behavior, focusing on the case of social dynamics. Resume I am currently a PostDoc in the Aramis Lab at the Brain & Spine Institute working with Mario Chavez and Fabrizio De Vico Fallani. I am also the Chair of the Young Researchers Network on Complex Systems, the younger branch of the Complex Systems Society. I earned my PhD in applied mathematics from Queen Mary University of London working in the complex systems and networks group of Vito Latora and as a part of the European Project LASAGNE on multilayer networks. Before that, I got an undergraduate and a master degree in theoretical physics from Sapienza University of Rome. You can find more information about me at: http://www.maths.qmul.ac.uk/~battiston/

Mezghani Manel

16 Mars 2017 – Salle 24-25/405

Lanalyse des données issues des réseaux sociaux pose des questions au niveau de la qualité des informations (cest à dire des informations identifiées, fiables et exactes) car on y trouve par exemple du spam social et des rumeurs, ce qui accroît la difficulté dobtenir des informations précises et pertinentes. Mon projet de recherche est axé sur la qualité des données dans les réseaux sociaux. Cette direction de recherche est très intéressante car elle touche différents domaines et peut être utile dans beaucoup dapplications. En effet, pouvoir évaluer la qualité de linformation peut être utilisé par exemple pour éliminer des spams/spammeurs, pour éviter danalyser des rumeurs, etc. Ceci permet par exemple davoir un meilleur résultat de recherche, déviter de mauvaises recommandations de produits ou de diffuser une bonne information aux utilisateurs. Une voie pertinente pour contribuer à lamélioration de la qualité de linformation et que je compte explorer est la détection et lanalyse de motifs récurrents des phénomènes sociaux (par exemple: spam, rumeur) au cours du temps dans les graphes de données issus des réseaux sociaux. Ceci permet de filtrer des données erronées et ainsi elles contribuent à fournir des données nettoyées qui peuvent être utilisées dans dautres applications. Ce séminaire sera loccasion de présenter mes travaux de recherche associé à lanalyse des réseaux sociaux; et de discuter de mon projet de recherche.

Damien Nogues

salle 24-25/405 à 11h00

La détection dintrusion est une problématique importante en cyber-sécurité. Cependant, de nombreuses approches classiques utilisent une approche à base de règles. Pour chaque attaque, unesignature de détection est créée. Ces approches ne permettent donc pas de détecter de nouvelles attaques, ou des attaques connues mais modifiées. Pour cette raison, nous nous sommesintéressés aux approches de détection danomalies. Ces méthodes utilisent des techniques dapprentissage non supervisé pour détecter des déviations à la norme. Ainsi, les techniques declustering sont fréquemment utilisées. Mais les algorithmes classiques de clustering, tel que k-means, ne sont pas très adaptés à ce problème. En effet, ces derniers nécessitent souvent de fixera priori le nombre de classes désiré. De plus, ces algorithmes sont en général conçus pour traiter des données quantitatives, ou parfois qualitatives. En détection dintrusions, les données sontsouvent hétérogènes. Nous détaillerons donc un nouveau critère de clustering adapté aux données hétérogènes, ainsi quun algorithme doptimisation de ce critère. Enfin, nous verronscomment il est possible dutiliser la partition obtenue pour effectuer une détection danomalies efficace sur des flux réseaux.

Maximilien Danisch

Campus Jussieu, Salle 26-00/124 à 11h00

Real-world graphs (a.k.a. complex networks) are ubiquitous: the web, Facebook, Twitter, brain networks, protein interaction networks, etc. Studying these graphs and solving computational problems on them (say maximum clique, partitioning or dense subgraph) has applications in many fields.I will first show that the structure of some real-world graphs is special. In particular, they are not adversarial and some difficult problems (say NP-complete or NP-hard problems) can be solved on some huge real-world graphs (say 2G edges or more).I will then present two works along the lines of « understanding and leveraging the structure of real-world graphs in order to build better algorithms »: (i) Enumerating all k-cliques and (ii) Computing the density-friendly graph decomposition.

Nicolas Bousquet

15 Novembre 2016, salle 26-00/124

We consider cooperative games where the viability of a coalition is determined by whether or not its members have the ability to communicate amongst themselves. This necessary condition for viability was proposed by Myerson and is modeled via an interaction graph; a coalition S of vertices is then viable if and only if the graph induced graph S is connected. The non-emptiness of the core of a coalition game can be tested by a well-known covering LP. Moreover, the integrality gap of its dual packing LP defines exactly the multiplicative least-core and the relative cost of stability of the coalition game. This gap is upper bounded by the packing-covering ratio which is known to be at most the treewidth of the interaction graph plus one. We examine the packing-covering ratio and integrality gaps of graphical coalition games in more detail. First we introduce a new graph parameter, called the vinewidth (a parameter derived from the treewidth), which characterizes the worst packing-covering ratio. Then we will show that this new parameter correctly evaluates both primal and dual integrality gaps. Joint work with Zhentao Li and Adrian Vetta.

Ingo Scholtes

Mercredi 19 Octobre 2016à 11h00 salle 24-25/405

Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the specific ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Summarizing our recent research in this area, in this talk I will introduce a framework that allows to analyze temporal networks with non-Markovian characteristics. The framework is based on higher-order aggregate networks, a simple generalization of the commonly used static representation of temporal network data. I will show that spectral properties of such higher-order aggregate networks can explain the slow-down of diffusion processes compared to aggregate networks, which has been observed in a number of empirical data sets. I further show that we can derive an exact analytical prediction for the magnitude of this change compared to the weighted, time-aggregate network. I finally present recent results on the analysis of node centralities in non-Markovian temporal networks, concluding that this approach provides interesting perspectives for (i) temporal community detection by spectral clustering, (ii) refined measures of centrality for time-evolving networks, and (iii) analytical studies of dynamical processes in complex systems with time-evolving interaction topologies.

David Coudert

Salle 24-25/405

The Gromov hyperbolicity is an important parameter for analyzing complex networks since it is a measure of the tree-likeness of a graph from a metric perspective. This notion has been recently applied in different contexts, such as the design of routing schemes, network security, computational biology, the analysis of graph algorithms, and the classification of complex networks. For instance, it gives bounds on the best possible stretch of some greedy-routing schemes in Internet-like graphs. The best known algorithm for computing hyperbolicity has running-time O(n^{3.69}), which is clearly prohibitive for big graphs. In this talk, we will present recent advances on the computation of this parameter. We will present an algorithm that performs well in practice. Although its time complexity is in O(n^4), it can compute the hyperbolicity of graphs with 100.000 nodes in short time. We will discuss the limitations of this algorithm and related open problems. We will also provide some results on the time complexity lower bound. In addition, we will show how to use some simple properties of the graph to get tight bounds on its hyperbolicity, e.g., being bipartite, a line graph, vertex transitive, etc. These properties are used for establishing bounds on the hyperbolicity of various interconnection network topologies proposed for large data centers.

Mattias Mano

Vendredi 30 Septembre à 10h30, Salle 26-00/332

Slides

Over the past years, online communities have considerably grown. Especially, a new kind of forums emerged: the « Questions and Answers » (Q&A) forums. They cover lots of different fields, from opinion questions (such as Yahoo! Answers, Reddit, Quora, …) to really technical questions in Computer Science (Stack Overflow, Bugzilla, …) or in Mathematics (Math Overflow). We study the opinion forum « Reddit – Change My View ». Participants debate on society subject. Using graph theory modeling, I wonder if structural informations of the discussion (graph) are sufficient enough to be characteristic of what happen in the discussion.