Roberto Interdonato

Cirad - La recherche agronomique pour le développement | CIRAD · UMR TETIS (Territoire, Environenment, Télédétection et Information Spatiale)

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagg...

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related field...

An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of t...

The Senegalese delta, like many other agricultural territories in the Global South, is experiencing changes in agricultural trajectory. These changes are related to the promotion of competitive and performance-based forms of agriculture. In a context of tense relations between farmers and herders, the quest for equitable access to land, which is a...

A variety of remote sensing applications call for automatic optical classification of satellite images. Recently, satellite missions, such as Sentinel-2, allow us to capture images in real-time of the Earth’s scenario. The classification of this large amount of data requires increasingly precise and fast methods, which must take into account not on...

This dataset is composed by spatial (e.g. location) and thematic (e.g. diseases, symptoms, virus) entities concerning avian influenza in social media (textual) data in English. It was created from three corpora: the first one includes 10 transcriptions of YouTube videos and 70 tweets manually annotated. The second corpus is composed by the same tex...

Purpose Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online newspapers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit s...

Graph Neural Networks (GNNs) are powerful tools that are nowadays reaching state of the art performances in a plethora of different tasks such as node classification, link prediction and graph classification. A challenging aspect in this context is to redefine basic deep learning operations, such as convolution, on graph-like structures, where node...

We present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-AgStream that accelerates the adoption of AI in...

After many years of decline, hunger in Africa is growing again. This represents a global societal issue that all disciplines concerned with data analysis are facing. The rapid and accurate identification of food insecurity situations is a complex challenge. Although a number of food security alert and monitoring systems exist in food insecure count...

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 experime...

The articles in this special section focus on the reloading of feature-rich information networks. The growing availability of multi-facetedrelational data gives rise to unprecedented opportunities for unveiling complex real-world behaviors and phenomena. This also supports the proliferation of complex network models where the expressive power of th...

Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the s...

Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network param...

Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible ind...

The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks assoc...

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently explo...

Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones...

Nowadays, great quantities of data are produced by a large and diverse family of sensors (e.g., remote sensors, biochemical sensors, wearable devices), which typically measure multiple variables over time, resulting in data streams that can be profitably organized as multivariate time-series. In practical scenarios, the speed at which such informat...

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...

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...

Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the...

Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes. Timely and accurate land use/land cover mapping can support this vision by providing useful information at fine scale. Here, a deep learning approach is proposed to deal with multi-source land cover mapping at...

Due to the proliferation of Earth Observation programmes, information at different spatial, spectral and temporal resolution is collected by means of various sensors (optical, radar, hyperspectral, LiDAR, etc.). Despite such abundance of information, it is not always possible to obtain a complete coverage of the same area (especially for large ones...

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5...

Large Scale Land Acquisitions (LSLAs) by private companies or states have seen a sudden increase in recent years, mainly due to combined and increasing demands for biofuel (i.e., caused by the increase in oil prices) and food (i.e., caused by the increase in world population and changes in dietary habits). These highly controversial phenomena raise...

Identifying food insecurity situations timely and accurately is a complex challenge. To prevent food crisis and design appropriate interventions, several food security warning and monitoring systems are very active in food-insecure countries. However, the limited types of data selected and the limitations of data processing methods used make it dif...

The workshop program of the Association for the Advancement of Artificial Intelligence’s 13th International Conference on Web and Social Media was held at the Bavarian School of Public Policy in Munich, Germany on June 11, 2019. There were five full-day workshops, one half-day workshop, and the annual evening Science Slam in the program. The procee...

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at highspatial resolution and high revisit time, respectively, radar and optical imagesthat support a wide range of Earth surface monitoring tasks such as LandUse/Land Cover mapping. A long-standing challenge in the remote sensingcommunity is about how to efficiently exploit mu...

Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network param...

Obtaining relevant timely information during crisis events is a challenging task that can be fundamental to handle the consequences deriving from both unexpected events (e.g., terrorist attacks) and partially predictable ones (i.e., natural disasters). Even though microblogging-based online social networks (e.g., Twitter) have become an attractive...

A multiplex network models different modes of interaction among same-type entities. A great deal of attention has been devoted to the community detection problem in multiplex networks, that is, revealing meaningful patterns of node groupings into communities by considering different types of interactions among them. In this article, we provide the...

The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2),...

Nowadays, the massive and horizontal diffusion of online social networks (OSNs) allows users to uncover different aspects about themselves through the use of multiple online platforms. In this respect, linking the accounts related to the same individual enables a more complete and thorough analysis of online behaviors. Indeed, the complexity of sce...

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has bee...

Abstract The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilisti...

Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary info...

Radar and Optical Satellite Image Time Series (SITS) are sources of information that are commonly employed to monitor earth surfaces for tasks related to ecology, agriculture, mobility, land management planning and land cover monitoring. Many studies have been conducted using one of the two sources, but how to smartly combine the complementary info...

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been...

While being long researched in social science and computer–human interaction, lurking behaviors in online social networks (OSNs) have been computationally studied only in recent years. Remarkably, determining lurking behaviors has been modeled as an unsupervised, eigenvector-centrality-based ranking problem, and it has been shown that lurkers can e...

Attributed network models have seen an increasing success in recent years, thanks to their informative power and to their ability to model complex networked relations that characterize most real-world phenomena. Their use has been attractive to communities in different disciplines such as computer science, physics, social science, as well as in int...

Research on influence maximization has often to cope with marketing needs relating to the propagation of information towards specific users. However, little attention has been paid to the fact that the success of an information diffusion campaign might depend not only on the number of the initial influencers to be detected but also on their diversi...

This chapter summarizes main literature and relating findings from social science and human-computer interaction research, focusing on: the different interpretations of lurking and related implications, the motivational factors underlying this kind of user behavior, and the main criteria to promote delurking of lurkers.

This chapter ends the brief offering a summary of the main topics discussed and providing suggestions for future research.

In this chapter, we discuss computational approaches to identify and rank lurkers in online social networks. We begin with a formal definition of topology-driven lurking and a detailed description of a family of centrality methods specifically conceived for ranking lurkers solely based on network topology, namely LurkerRank. To better model dynamic...

In this chapter, we describe the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. A study carried out on different complex network models shows how the Lurker Game is suitable to model lurking dynamics, and how the adoption of rew...

In this chapter, we discuss main remarks and findings raised from the experimental evaluations of lurker rank methods conducted over several real-world OSNs, such as Twitter, FriendFeed, Flickr, Instagram, and Google+. We discuss how the ranking results produced by LurkerRank are effective in identifying and characterizing users at different grades...

Encouraging lurkers to more actively participate in the OSN life, a.k.a. delurking, is desirable in order to make lurkers’ social capital available to other users. In this chapter, we discuss in detail the delurking problem and computational approaches to solve it. We first provide an overview of works focusing on user engagement methodologies to u...

Identifying and mining lurkers finds application in a variety of OSNs other than social media platforms. In this chapter, we put evidence on the pervasiveness of the notion of lurking, utilizing collaboration networks and trust networks as two cases in point. As regards collaboration networks, we focus on a parallel between lurkers and vicarious le...

The social boundary spanning theory explains how OSN users share and transfer their knowledge through the network. In this chapter, we consider two aspects related to the role of lurkers in boundary spanning contexts. In the first part, we concentrate on the relation between lurkers and OSN communities, discussing how the user’s capability of acros...

When visiting a touristic venue, building personalized itineraries is often non-trivial, mainly because of the variety of types of points-of-interest (PoIs) that might be considered by an individual. Several online platforms exist to support the tourists by providing them with detailed PoI-related information in a certain area, such as routes, dist...

The problem of local community detection in graphs refers to the identification of a community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks,...

Lurkers are silent members of a social network (SN) who gain benefit from others’ information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they can be social capital holders. Within this view, a major goal is to...

The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework f...

The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework f...

Given the increasing volume and impact of online social interactions in various aspects of life, inferring how a user should be trusted becomes a matter of crucial importance, which can strongly bias any decision process. Existing trust inference algorithms are based on the propagation and aggregation of trust values. However, trust opinions are su...

Lurking is a complex user-behavioral phenomenon that occurs in all large-scale online communities and social networks. It generally refers to the behavior characterizing users that benefit from the information produced by others in the community without actively contributing back to the production of social content. The amount and evolution of lurk...

Research on social trust analysis has traditionally focused on the trustworthy/untrustworthy behaviors that are exhibited by active users. By contrast, due to their inherent reticence to regularly contribute to the online community life, the silent users in a social network, a.k.a. lurkers, have been taken out of consideration so far. Nevertheless,...

Mining the silent members of an online community, also called lurkers, has been recognized as an important problem that accompanies the extensive use of online social networks (OSNs). Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in an OSN. However, they are limited to use only structural properties of the...

Lurkers are silent members of a social network (SN) who gain benefit from others' information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they are social capital holders. Within this view, a major goal is to de...

This paper overviews recent research findings concerning a new challenging problem in information networks, namely identifying and ranking silent nodes. We present three case studies which show how silent nodes’ behavior maps to different situations in computer networks, online social networks, and online collaboration networks, and we discuss majo...

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related field...

Mining the silent members, also called lurkers, of an online community has been recognized as an important problem that accompanies the extensive use of social networks. Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in social networks, however they ignore any information concerning the time dimension. In t...

In this paper, we study the problem of structural sense ranking for tree data using a multi-relational PageRank approach. By considering multiple types of structural relations, the original tree structural context is better leveraged and hence used to improve the ranking of the senses associated to the tree elements. Upon this intuition, we advance...

An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. Most existing works on the topic have focused on a specific application domain, thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we discuss a versatile packa...

Despite being a topic of growing interest in social learning theory, vicarious learning has not been well-studied so far in digital library related tasks. In this paper, we address a novel ranking problem in research collaboration networks, which focuses on the role of vicarious learner. We introduce a topology-driven vicarious learning definition...