Currently, link recommendation has gained more attention as networked data becomes abundant in several scenarios. However, existing methods for this task have failed in considering solely the structure of dynamic networks for improved performance and accuracy. Hence, in this work, we present a methodology based on the use of multiple topological metrics in order to achieve prospective link recommendations considering time constraints. The combination of such metrics is used as input to binary classification algorithms that state whether two pairs of authors will/should define a link. We experimented with five algorithms, what allowed us to reach high rates of accuracy and to evaluate the different classification paradigms. Our results also demonstrated that time parameters and the activity profile of the authors can significantly influence the recommendation. In the context of DBLP, this research is strategic as it may assist on identifying potential partners, research groups with similar themes, research competition (absence of obvious links), and related work.

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The use of graph theory for analyzing network-like data has gained central importance with the rise of the Web 2.0. However, many graph-based techniques are not welldisseminated and neither explored at their full potential, what might depend on a complimentary approach achieved with the combination of multiple techniques. This paper describes the systematic use of graph-based techniques of different types (multimodal) combining the resultant analytical insights around a common domain, the Digital Bibliography & Library Project (DBLP). To do so, we introduce an analytical ensemble based on statistical (degree, and weakly-connected components distribution), topological (average clustering coefficient, and effective diameter evolution), algorithmic (link prediction/machine learning), and algebraic techniques to inspect non-evident features of DBLP at the same time that we interpret the heterogeneous discoveries found along the work. As a result, we have put together a set of techniques demonstrating over DBLP what we call multimodal analysis, an innovative process of information understanding that demands a wide technical knowledge and a deep understanding of the data domain. We expect that our methodology and our findings will foster other multimodal analyses and also that they will bring light over the Computer Science research.

Complex networks provide a means to describe cities through their street mesh, expressing characteristics that refer to the structure and organization of an urban zone. Although other studies have used complex networks to model street meshes, we observed a lack of methods to characterize the relationship between cities by using their topological features. Accordingly, this paper aims to describe interactions between cities by using vectors of topological features extracted from their street meshes represented as complex networks. The methodology of this study is based on the use of digital maps. Over the computational representation of such maps, we extract global complex-network features that embody the characteristics of the cities. These vectors allow for the use of multidimensional projection and clustering techniques, enabling a similarity-based comparison of the street meshes. We experiment with 645 cities from the Brazilian state of Sao Paulo. Our results show how the joint of global features describes urban indicators that are deeprooted in the network's topology and how they reveal characteristics and similarities among sets of cities that are separated from each other.

What if a successful company starts to receive a torrent of low-valued (one or two stars) recommendations in its mobile apps from multiple users within a short (say one month) period of time? Is it legitimate evidence that the apps have lost in quality, or an intentional plan (via lockstep behavior) to steal market share through defamation? In the case of a systematic attack to one's reputation, it might not be possible to manually discern between legitimate and fraudulent interaction within the huge universe of possibilities of user-product recommendation. Previous works have focused on this issue, but none of them took into account the context, modeling, and scale that we consider in this paper. Here, we propose the novel method Online-Recommendation Fraud ExcLuder (ORFEL) to detect defamation and/or illegitimate promotion of online products by using vertex-centric asynchronous parallel processing of bipartite (usersproducts) graphs. With an innovative algorithm, our results demonstrate both efficacy and efficiency -over 95% of potential attacks were detected, and ORFEL was at least two orders of magnitude faster than the state-of-the-art. Over a novel methodology, our main contributions are: (1) a new algorithmic solution; (2) one scalable approach; and (3) a novel context and modeling of the problem, which now addresses both defamation and illegitimate promotion. Our work deals with relevant issues of the Web 2.0, potentially augmenting the credibility of online recommendation to prevent losses to both customers and vendors.

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GIMENES, GABRIEL P. Data analysis over large-scale graphs using vertex-centric asynchronous parallel processing. 2020. 89 p. Tese (Doutorado em Ciências

Rodrigues Jr, who patiently guided and helped me throughout the journey towards this dissertation. I would also like to thank my professors and colleagues for sharing their knowledge, thoughts, classes, and spare time with me. I would also like to thank FAPESP for financially supporting my research, and to the department staff for always being ready to help. Finally, I would like to thank my girlfriend Gabrielle for her comprehension and care, and my family for their continuous support.