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Details

  • Name

    Pedro Manuel Ribeiro
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    03rd May 2010
Publications

2019

Temporal network alignment via GoT-WAVE

Authors
Aparicio, D; Ribeiro, P; Milenkovic, T; Silva, F;

Publication
Bioinformatics

Abstract

2019

TENSORCAST: forecasting and mining with coupled tensors

Authors
Araujo, M; Ribeiro, P; Song, HA; Faloutsos, C;

Publication
KNOWLEDGE AND INFORMATION SYSTEMS

Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.

2019

Finding Dominant Nodes Using Graphlets

Authors
Aparício, D; Ribeiro, P; Silva, F; Silva, JMB;

Publication
Studies in Computational Intelligence

Abstract
Finding important nodes is a classic task in network science. Nodes are important depending on the context; e.g., they can be (i) nodes that, when removed, cause the network to collapse or (ii) influential spreaders (e.g., of information, or of diseases). Typically, central nodes are assumed to be important, and numerous network centrality measures have been proposed such as the degree centrality, the betweenness centrality, and the subgraph centrality. However, centrality measures are not tailored to capture one particular kind of important nodes: dominant nodes. We define dominant nodes as nodes that dominate many others and are not dominated by many others. We then propose a general graphlet-based measure of node dominance called graphlet-dominance (GD). We analyze how GD differs from traditional network centrality measures. We also study how certain parameters (namely the importance of dominating versus not being dominated and indirect versus direct dominances) influence GD. Finally, we apply GD to author ranking and verify that GD is superior to PageRank in four of the five citation networks tested. © 2020, Springer Nature Switzerland AG.

2019

An efficient approach for counting occurring induced subgraphs

Authors
Grácio, L; Ribeiro, P;

Publication
Springer Proceedings in Complexity

Abstract
Counting subgraph occurrences is a hard but very important task in complex network analysis, with applications in concepts such as network motifs or graphlet degree distributions. In this paper we present a novel approach for this task that takes advantage of knowing that a large fraction of subgraph types does not appear at all on real-world networks. We describe a pattern-growth methodology that is able to iteratively build subgraph patterns that do not contain smaller non-occurring subgraphs, significantly pruning the search space. By using the g-trie data structure, we are able to efficiently only count those subgraphs that we are interested in, reducing the total computation time. The obtained experimental results are very promising allowing us to avoid the computation of up to 99.78% of all possible subgraph patterns. This showcases the potential of this approach and paves the way for reaching previously unattainable subgraph sizes. © Springer Nature Switzerland AG 2019.

2018

TensorCast: Forecasting Time-Evolving Networks with Contextual Information

Authors
Araujo, M; Pinto Ribeiro, PM; Faloutsos, C;

Publication
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden.

Abstract

Supervised
thesis

2017

Communities and Anomaly Detection in Large Edged-Labeled Graphs

Author
Miguel Ramos de Araújo

Institution
UP-FCUP

2017

Improving the search for multi-relational concepts in ILP

Author
Alberto José Rajão Barbosa

Institution
UP-FCUP

2017

CompAlg - Ferramenta de Ensino e Aprendizagem da Lógica de Programação

Author
Augusto Manuel Bilabila

Institution
UP-FCUP

2017

Counting subgraphs: from static to dynamic networks

Author
Pedro Miguel Reis Bento Paredes

Institution
UP-FCUP

2016

Bomberman as an Artificial Intelligence Platform

Author
Manuel António da Cruz Lopes

Institution
UP-FCUP