Jean‐Loup Guillaume
Biographic Data
| ID | 4972483 |
|---|---|
| NAME | Jean‐Loup Guillaume |
| GIVEN NAMES | Jean‐Loup |
| FAMILY NAME | Guillaume |
| SIGNATURE | GUILLAUME J L |
| AFFILIATIONS | La Rochelle Université |
| ORCID | 0000-0002-4615-1563 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
A survey on bipartite graphs embedding
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
Modeling cascade formation in Twitter amidst mentions and retweets
Multi-ego-centered communities in practice
Towards multi-ego-centred communities
In this paper, we investigate the notion of ego-centred community structure, i.e., the community structure around a given node. We propose a new similarity measure between nodes based on opinion dynamics to study such a structure. We call it the carryover opinion. In addition to be parameter-free, the carryover opinion can be calculated in a very time-efficient way and can thus be used in very large graphs. We also go further in the idea of ego-c…
Fast unfolding of communities in large networks
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…
Fast unfolding of communities in large networks
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…
Towards multi-ego-centred communities
In this paper, we investigate the notion of ego-centred community structure, i.e., the community structure around a given node. We propose a new similarity measure between nodes based on opinion dynamics to study such a structure. We call it the carryover opinion. In addition to be parameter-free, the carryover opinion can be calculated in a very time-efficient way and can thus be used in very large graphs. We also go further in the idea of ego-c…
Multi-ego-centered communities in practice
Modeling cascade formation in Twitter amidst mentions and retweets
A lightweight and multilingual framework for crisis information extraction from Twitter data
A survey on bipartite graphs embedding
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 (6 works) · Computer Science (6 works) · Artificial Intelligence (4 works) · Mathematics (4 works) · Opinion Dynamics and Social Influence (4 works) · Community structure (3 works) · Data mining (3 works) · Engineering (3 works) · Psychology (3 works) · Theoretical Computer Science (3 works)