The emergence of large network data necessitates parallelization This article only introduced one of the many potential algorithms associated with community detection. Our approach begins with an arbitrarily partitioned distributed graph The Louvain algorithm is one of the most popular algorithms for community detection. This technical report presents one of the most This paper presents an enhancement of the well-known Louvain algorithm for community detection with modularity maximization which was introduced in [16]. The concept and benefit are In most real-world networks, the nodes/vertices tend to be organized into tightly-knit modules known as communities or clusters, such that nodes within a community are more likely to be "related" to one In this paper, we show that the Louvain algorithm has a major problem, for both modularity and CPM. Observing that existing implementations suffer from inaccurate pruning and inefficient intermediate To improve the detection efficiency of large-scale networks, an improved Fast Louvain algorithm is proposed. The Louvain method – named after the University of Louvain where Blondel et al. This paper presents an enhancement of the well-known Lou-vain algorithm for community detection with modularity maximization which was introduced in [16]. Blondel, Jean-Loup Guillaume, Renaud Lambiotte and Etienne Lefebvre in this paper in . It was developed as a modification of the Louvain method. To do so, we improve the speed of However, it remains a challenging and open research prob-lem to parallelize a community detection algorithm and make it scalable to tackle real-world large graphs. Our modularity function 𝐹 2 overcomes certain disadvantages of the In this paper, we will add a phase of fine-tuning local communities after the first phase of the Louvain algorithm using the Random Walk Graph Partition Algorithm. The algorithm optimizes the The Louvain method for community detection is a greedy optimization method intended to extract non-overlapping communities from large networks created by In conclusion, this report presents our parallel multicore implementation of the Louvain algorithm — a high quality community detection method, which, as far as we are aware, stands as the most efficient In this paper, we propose a modularity function 𝐹 2 as a new objective function. The Louvain algorithm is a In this paper, we present the design of a distributed memory implementation of the Louvain algorithm for parallel community detection. The Louvain algorithm is a Citations (24) References (34) Abstract This paper presents an enhancement of the well-known Louvain algorithm for community detection with Louvain method is the most efficient algorithm to detect communities in large scale network. The algorithm may yield arbitrarily badly connected communities, over and above the well-known for the network, thereby giving access to different resolutions of community detection. The algorithm may yield arbitrarily badly connected commu-nities, over and above the well-known The Louvain algorithm was originally developed for optimizing modularity, but has been applied to a variety of methods. It is shown to outperform all One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. Expansion of the Louvain Algorithm is carried out by forming a community based on connections between nodes Our goal in this paper is to quickly detect the community structure of a large network using the Louvain algorithm. It is shown to outperform all other known community detection method in terms of computation time. This is mainly because graphs The Louvain algorithm is a hierarchical clustering algorithm, that recursively merges communities into a single node and executes the modularity clustering on the condensed graphs. Like the Louvain method, the Paper proposed the clique-based Louvain algorithm (CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the Louvain algorithm is a well-known and efficient method for detecting communities or clusters in social and information networks (graphs). This is mainly because graphs The Leiden algorithm is a community detection algorithm developed by Traag et al [1] at Leiden University. Iterating the algorithm worsens the problem. developed the algorithm – finds communities by optimizing modularity The goal of this paper is to shed light on the inner-workings of Louvain; only if we understand Louvain, can we rely on it and further improve it. We show that this algorithm has a major defect that largely went unnoticed until The Louvain algorithm is one of the most popular algorithms for community detection. There are many various algorithms In this paper, two algorithm based on agglomerative method (Louvain and Leiden) are introduced and reviewed. As such, speeding up the Louvain algorithm, enables the analysis of Louvain’s Algorithm for Community Detection: Louvain’s algorithm was proposed by Vincent D. Our method is a heuristic method that is based on modularity optimization. Observing that existing implementations suffer from inaccurate pruning and inefficient intermediate Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This paper presented our parallel multicore implementation of the Louvain algorithm—a high quality community detection method, which, as far as we are aware, stands as the most efficient We propose a simple method to extract the community structure of large networks. Our algorithm adopts a novel The Louvain algorithm is very popular but may yield disconnected and badly connected communities. For more information We will present improvements to famous algorithms for community detection, namely Newman's spectral method algorithm and the Louvain algorithm. The Newman algorithm begins by Louvain algorithm 🚨 This page is a work in progress. To achieve this goal, we study the behavior of Louvain in the In this paper we present and evaluate a parallel community detection algorithm derived from the state-of-the-art Louvain modularity maximization method. We propose a simple method to extract the community structure of large networks. The Leiden algorithm guarantees γ-connected However, it remains a challenging and open research prob-lem to parallelize a community detection algorithm and make it scalable to tackle real-world large graphs. Although community detection in networks has been studied for many years, a high-speed and high-quality community detection algorithm is We would like to show you a description here but the site won’t allow us. Contrary to all the other community detection algorithms, the network size limits that we are facing with our algorithm Abstract. In this paper, we show that the Louvain algorithm has a major problem, for both modularity and CPM.
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