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Jean-Loup Guillaume

Biographic Data

ID1753754
NAMEJean-Loup Guillaume
GIVEN NAMESJean-Loup
FAMILY NAMEGuillaume
SIGNATUREGUILLAUME J
VERIFIEDNo
TOTAL WORKS5
TOTAL CITATIONS3
AUTHOR COUNT5
EDITOR COUNT0
FIRST PUBLICATION YEAR2008
LATEST PUBLICATION YEAR2023
H-INDEX1
  • A survey on bipartite graphs embedding

    Open Access•Edward Giamphy, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2023•References: 37

    Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a descr…

  • A lightweight and multilingual framework for crisis information extraction from Twitter data

    Open Access•Roberto Interdonato, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2019•Cited by: 3•References: 31

  • Modeling cascade formation in Twitter amidst mentions and retweets

    Open Access•Swagato Pramanik, Qinna Wang et al.•ARTICLE•Social Network Analysis and Mining•2017

  • Multi-ego-centered communities in practice

    Open Access•Maximilien Danisch, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2014

  • Fast unfolding of communities in large networks

    Open Access•Vincent D Blondel, Jean-Loup Guillaume et al.•ARTICLE•Journal of Statistical Mechanics:…•2008

    We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network…

  • A lightweight and multilingual framework for crisis information extraction from Twitter data

    Open Access•Roberto Interdonato, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2019•Cited by: 3•References: 31

  • Fast unfolding of communities in large networks

    Open Access•Vincent D Blondel, Jean-Loup Guillaume et al.•ARTICLE•Journal of Statistical Mechanics:…•2008

    We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network…

  • Multi-ego-centered communities in practice

    Open Access•Maximilien Danisch, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2014

  • Modeling cascade formation in Twitter amidst mentions and retweets

    Open Access•Swagato Pramanik, Qinna Wang et al.•ARTICLE•Social Network Analysis and Mining•2017

  • A lightweight and multilingual framework for crisis information extraction from Twitter data

    Open Access•Roberto Interdonato, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2019•Cited by: 3•References: 31

  • A survey on bipartite graphs embedding

    Open Access•Edward Giamphy, Jean-Loup Guillaume et al.•ARTICLE•Social Network Analysis and Mining•2023•References: 37

    Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a descr…

Complex Network Analysis Techniques (5 works) · Computer Science (5 works) · Artificial Intelligence (3 works) · Engineering (3 works) · Mathematics (3 works) · Opinion Dynamics and Social Influence (3 works) · Community structure (2 works) · Data mining (2 works) · Data science (2 works) · Graph (2 works)

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