Cécile Favre

Information diffusion in online social networks: a survey

Online social networks play a major role in the spread of information at very large scale. A lot of effort have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper, we propose mention-anomaly-based event detection (MABED), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e., mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users’ interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.

Mention-anomaly-based event detection and tracking in Twitter

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.

Evolution of data warehouses' optimization: a workload perspective

Data warehouse (DW) evolution usually means evolution of its model. However, a decision support system is composed of the DW and of several other components, such as optimization structures like indices or materialized views. Thus, dealing with the DW evolution also implies dealing with the maintenance of these structures. However, propagating evolution to these structures thereby maintaining the coherence with the evolutions on the DW is not always enough. In some cases propagation is not sufficient and redeployment of optimization strategies may be required. Selection of optimization strategies is mainly based on workload, corresponding to user queries. In this paper, we propose to make the workload evolve in response to DW schema evolution. The objective is to avoid waiting for a new workload from the updated DW model. We propose to maintain existing queries coherent and create new queries to deal with probable future analysis needs.

SONDY: an open source platform for social dynamics mining and analysis

This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible. In the demo, participants will be invited to explore information from several datasets of various sizes and origins (such as a dataset consisting of 7,874,772 messages published by 1,697,759 Twitter users during a period of 7 days) and apply the different functionalities of the platform in real-time.

Combining OLAP and information networks for bibliographic data analysis: a survey

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

OLAP on Information Networks: A New Framework for Dealing with Bibliographic Data

In the context of decision making, data warehouses support OLAP technology and they have been very useful for efficient analysis onto structured data. For several years, OLAP is also used to analyze and visualize more complex data. Now, many data sets of interest can be described as a linked collection of interrelated objects. They could be represented as heterogeneous information networks, in which there are multiple object and link types. In this paper, we are focusing on bibliographic data. This type of data constitutes a rich source that is the starting point of research on bibliometrics, scientometrics domains. In this context, we discuss the interest of combining information networks, OLAP and data mining technologies. We propose a framework to materialize this combination and discuss the main challenges to build this framework. The basic idea is to be able to analyze various networks built from the bibliographic data representing different points of view (authors networks, citations networks...) and their dynamic.

Enhancing flexibility and expressivity of contextual hierarchies

Data warehouses are nowadays extensively used to perform analyses on huge volume of data. This success is partly due to the capacity of considering data at several granularity levels thanks to the use of hierarchies. However, in previous work, we showed that the experts knowledge was not much considered in the generalization process. To overcome this drawback, we introduced a new category of hierarchies, namely the contextual hierarchies. Unfortunately, in contrast to the complexity of expert knowledge that should be considered, the knowledge definition process was too rigid. In this paper, we extend these hierarchies and their related techniques to drastically increase their flexibility and expressivity. To this purpose, we adopt a fuzzy-based methodology which allows to express expert knowledge in a very convenient way. Experiment results obtained on synthetic datasets show that the contextual generalization process is very fast and can thus be used in practice.

Graphs enriched by cubes for OLAP on bibliographic networks

With the recent growth of bibliographic data, many research fields work on defining new techniques for their analysis. In this context, data could be represented as heterogeneous networks. In order to analyse information networks in a multidimensional way, online analytical processing OLAP may be a relevant solution but it must be adapted for networked data by considering nodes and edges. A first approach that has been proposed in the literature consists in building cubes of graphs. In a different and complementary way, our proposal consists in enriching graphs with cubes. Indeed, the nodes or/and edges of the considered network are described by a cube. It allows interesting analyses for the user who can navigate within a graph enriched by cubes according to different granularity levels, with dedicated operators. We implemented our approach and performed an experimental study on a real dataset to show the interest of our proposal.

Efficient online mining of large databases

Great efforts have been achieved to apply data mining algorithms onto large databases. However, long processing times remain a practical issue. This paper presents a framework to offer to database users online operators for mining large databases without size limit, in acceptable processing times. First, we integrate decision tree algorithms directly into database management systems. We are thus only limited by disc capacity and not by main memory. However, disc accesses still induce long response times. Hence, we propose two optimisations in a second step: reducing the size of the learning database by building its corresponding contingency table and reducing the number of database accesses by exploiting bitmap indices. Thus, the various decision tree based methods we implemented within Oracle deal with contingency tables or bitmap indices rather than with the whole training set. Experimentations performed show the efficiency of our integrated methods.