VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks.

Many real world data can be modeled by a graph with a set of nodes interconnected to each other by multiple relationships. Such a rich graph is called multilayer graph or network. Providing useful visualization tools to support the query process for such graphs is challenging. Although many approaches have addressed the visual query construction, few efforts have been done to provide a contextualized exploration of query results and suggestion strategies to refine the original query. This is due to several issues such as i) the size of the graphs ii) the large number of retrieved results and iii) the way they can be organized to facilitate their exploration. In this article, we present VERTIGo, a novel visual platform to query, explore and support the analysis of large multilayer graphs. VERTIGo provides coordinated views to navigate and explore the large set of retrieved results at different granularity levels. In addition, the proposed system supports the refinement of the query by visual suggestions to guide the user through the exploration process. Two examples and a user study demonstrate how VERTIGo can be used to perform visual analysis (query, exploration, and suggestion) on real world multilayer networks.

Enhancing Graph-Based Semisupervised Learning via Knowledge-Aware Data Embedding.

Semisupervised learning (SSL) is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semisupervised strategies: the nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based SSL has mainly focused on two aspects: 1) the construction of the k -nearest neighbors graph and/or 2) the propagation algorithm providing the classification. Differently from the previous literature, in this article, we focus on the data representation with the aim of incorporating semisupervision earlier in the process. To this end, we propose an algorithm that learns a new knowledge-aware data embedding via an ensemble of semisupervised autoencoders to enhance a graph-based semisupervised classification. The experiments carried out on different classification tasks demonstrate the benefit of our approach.

Assessment of congruence between co-occurrence and functional networks: A new framework for revealing community assembly rules.

Describing how communities change over space and time is crucial to better understand and predict the functioning of ecosystems. We propose a new methodological framework, based on network theory and modularity concept, to determine which type of mechanisms (i.e. deterministic versus stochastic processes) has the strongest influence on structuring communities. This framework is based on the computation and comparison of two networks: the co-occurrence (based on species abundances) and the functional networks (based on the species traits values). In this way we can assess whether the species belonging to a given functional group also belong to the same co-occurrence group. We adapted the Dg index of Gauzens et al. (2015) to analyze congruence between both networks. This offers the opportunity to identify which assembly rule(s) play(s) the major role in structuring the community. We illustrate our framework with two datasets corresponding to different faunal groups and ecosystems, and characterized by different scales (spatial and temporal scales). By considering both species abundance and multiple functional traits, our framework improves significantly the ability to discriminate the main assembly rules structuring the communities. This point is critical not only to understand community structuring but also its response to global changes and other disturbances.

A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data.

In this paper, we introduce a new approach of semisupervised anomaly detection that deals with categorical data. Given a training set of instances (all belonging to the normal class), we analyze the relationship among features for the extraction of a discriminative characterization of the anomalous instances. Our key idea is to build a model that characterizes the features of the normal instances and then use a set of distance-based techniques for the discrimination between the normal and the anomalous instances. We compare our approach with the state-of-the-art methods for semisupervised anomaly detection. We empirically show that a specifically designed technique for the management of the categorical data outperforms the general-purpose approaches. We also show that, in contrast with other approaches that are opaque because their decision cannot be easily understood, our proposed approach produces a discriminative model that can be easily interpreted and used for the exploration of the data.