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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.

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

2018

Graphlet-orbit Transitions (GoT): A fingerprint for temporal network comparison

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

Publication
PLOS ONE

Abstract

2018

Fast streaming small graph canonization

Authors
Paredes, P; Ribeiro, P;

Publication
Springer Proceedings in Complexity

Abstract
In this paper, we introduce the streaming graph canonization problem. Its goal is finding a canonical representation of a sequence of graphs in a stream. Our model of a stream fixes the graph’s vertices and allows for fully dynamic edge changes, meaning it permits both addition and removal of edges. Our focus is on small graphs, since small graph isomorphism is an important primitive of many subgraph-based metrics, like motif analysis or frequent subgraph mining. We present an efficient data structure to approach this problem, namely a graph isomorphism discrete finite automaton and showcase its efficiency when compared to a non-streaming-aware method that simply recomputes the isomorphism information from scratch in each iteration. © Springer International Publishing AG 2018.

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

Large Scale Parallel Subgraph Search

Author
Ahmad Naser Eddin

Institution
UP-FCUP