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See detailA Memory-Based Label Propagation Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Complex Networks and Their Applications VII (2019)

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of ... [more ▼]

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node implements memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks that MemLPA outperforms most of state-of-the-art community detection algorithms. [less ▲]

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See detailA Memory-Based Label Propagation Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Aiello, Luca Maria; Cherifi, Chantal; Cherifi, Hocine (Eds.) et al Complex Networks and Their Applications VII (2018, December 02)

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dis- similarity between them. Several methods have been proposed but many of ... [more ▼]

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dis- similarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node imple- ments memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world net- works that MemLPA outperforms most of state-of-the-art community detection algorithms. [less ▲]

Detailed reference viewed: 81 (21 UL)
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See detailMind the Gap: Gender and Computer Science Conferences
van Herck, Sytze UL; Fiscarelli, Antonio Maria UL

in Kreps, David; Ess, Charles; Leenen, Louise (Eds.) et al This Changes Everything - ICT and Climate Change: What Can We Do? 13th IFIP TC 9 International Conference on Human Choice and Computers, HCC13 2018. Held at the 24th IFIP World Computer Congress, WCC2018, Poznan, Poland, September 19-21, 2018, Proceedings. (2018)

Computer science research areas are often arbitrarily defined by researchers themselves based on their own opinions or on conference rankings. First, we aim to classify conferences in computer science in ... [more ▼]

Computer science research areas are often arbitrarily defined by researchers themselves based on their own opinions or on conference rankings. First, we aim to classify conferences in computer science in an automated and objective way based on topic modelling. We then study the topic relatedness of research areas to identify isolated disciplinary silos and clusters that display more interdisciplinarity and collaboration. Furthermore, we compare career length, publication growth rate and collaboration patterns for men and women in these research areas. [less ▲]

Detailed reference viewed: 38 (6 UL)
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See detailA Degenerate Agglomerative Hierarchical Clustering Algorithm for Community Detection
Fiscarelli, Antonio Maria UL; Beliakov, Aleksandr UL; Konchenko, Stanislav UL et al

in Nguyen, Ngoc Thanh; Hoang, Duong Hung; Hong, Tzung-Pei (Eds.) et al Intelligent Information and Database Systems (2018)

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and ... [more ▼]

Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and detecting such communities can be challenging, especially when they are densely connected. The method we propose is a degenerate agglomerative hierarchical clustering algorithm (DAHCA) that aims at finding a community structure in networks. We tested this method using common classes of graph benchmarks and compared it to some state-of-the-art community detection algorithms. [less ▲]

Detailed reference viewed: 73 (7 UL)