Fast detection of community structures using graph traversal in social networks

Basuchowdhuri, Partha; Sikdar, Satyaki; Nagarajan, Varsha; Mishra, Khusbu; Gupta, Surabhi; Majumder, Subhashis

doi:10.1007/s10115-018-1209-7

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…In addition, d c refers to the sum of degrees of nodes in a community such as C: Afterwards, we compared our proposed algorithm with some well-known and essential community detection algorithms. For this, we compared the Gravity algorithm with Louvain (Blondel et al 2008), LINCOM (Basuchowdhuri et al 2019), LexDFS (Creusefond et al 2017), LPA (Raghavan et al 2007), and Infomap (Rosvall and Bergstrom 2008). (Basuchowdhuri et al 2019) compares Louvain, LINCOM, LexDFS, LPA, and Infomap with each other.…”

Section: Resultsmentioning

confidence: 99%

“…For this, we compared the Gravity algorithm with Louvain (Blondel et al 2008), LINCOM (Basuchowdhuri et al 2019), LexDFS (Creusefond et al 2017), LPA (Raghavan et al 2007), and Infomap (Rosvall and Bergstrom 2008). (Basuchowdhuri et al 2019) compares Louvain, LINCOM, LexDFS, LPA, and Infomap with each other. In Table 5, we compare the speed of all algorithms with the speed of the slowest one.…”

Section: Resultsmentioning

confidence: 99%

“…Although its convergence is provable mathematically, it depends on precise knowledge about the iteration number of algorithms. On the other hand, the iteration number depends on the network parameters such as edge and vertex numbers (Basuchowdhuri et al 2019). Also, Creusefond et al proposed a new algorithm for community detection that is based on the LexDFS (Lexicographical Depth-First Search) traversal algorithm (Creusefond et al 2017).…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, a broker node is selected according to an objective function. Then it is placed in a correct community where the majority of its neighbours are located (Basuchowdhuri et al 2019).…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Gravity algorithm for the community detection of large-scale network

Arasteh

Alizadeh

Lee

2021

J Ambient Intell Human Comput

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Community detection refers to the task of finding groups of nodes in a network that share common properties. The identified groups are called communities, which have tight intra-connections and feeble inter-connections. For the large-scale networks, we need a stable algorithm to detect communities quickly and does not depend on previous knowledge about the possible communities and any special parameter tuning. Therefore, this paper introduces a novel algorithm for community detection that is inspired by the surface Gravity of astronomical objects. In this algorithm, each vertex of a network and its degree and size are metaphors for an astronomical object and its mass and radius, respectively. The algorithm defines the gravity force for each vertex of a network. So, as a dense astronomical object, a dense vertex has a high mass and a low radius and exerts a high gravity force on its neighbours. In this paper, we define a particular modularity gain function to evaluate the merging gain of two vertexes. The algorithm is very fast, and its computational complexity is from the order of O(n log n) . Although there is a trade-off between the modularity and speed of algorithms, the results showed that the suggested algorithm is much faster than the current well-known algorithms. Furthermore, it is reliable, stable and free from parameter tuning as well as predefined knowledge about communities. In this paper, the proposed algorithm is extended to run faster than the original Gravity. The extended Gravity algorithm divides a large scale network into some smaller sub-networks. Then the communities of each part are detected in serial and parallel modes. By preserving the modularity of detected communities, the extended Gravity is extremely faster than the original Gravity algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Gravity algorithm for the community detection of large-scale network

Arasteh

Alizadeh

Lee

2021

J Ambient Intell Human Comput

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“…Community structures exist in many networks in the real world, such as social networks, e-commerce networks, biological networks, routing networks, and so on. In recent years, community structure detection of complex networks has attracted much attention, and a series of complex network mining algorithms have been proposed [ 1 , 2 , 3 , 4 , 5 ]. The identification of community structure in the network has great application value.…”

Section: Introductionmentioning

confidence: 99%

Research on Community Detection in Complex Networks Based on Internode Attraction

Sheng

Liu

Chen

et al. 2020

Entropy

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With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

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Distributed Community Prediction for Social Graphs Based on Louvain Algorithm

Makris

Pettas

Pispirigos

2019

IFIP Advances in Information and Communication Technology

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Nowadays, the problem of community detection has become more and more challenging. With application in a wide range of fields such as sociology, digital marketing, bio-informatics, chemical engineering and computer science, the need for scalable and efficient solutions is strongly underlined. Especially, in the rapidly developed and widespread area of social media where the size of the corresponding networks exceeds the hundreds of millions of vertices in the average case. However, the standard sequential algorithms applications have practically proven not only infeasible but also terribly unscalable due to the excessive computation demands and the overdone resources prerequisites. Therefore, the introduction of compatible distributed machine learning solutions seems the most promising option to tackle this NP-hard class problem. The purpose of this work is to propose a novel distributed community detection methodology, based on the supervised community prediction concept that is extremely scalable, remarkably efficient and circumvent the intrinsic adversities of classic community detection approaches.

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Fast detection of community structures using graph traversal in social networks

Cited by 15 publications

References 31 publications

Gravity algorithm for the community detection of large-scale network

Gravity algorithm for the community detection of large-scale network

Research on Community Detection in Complex Networks Based on Internode Attraction

Distributed Community Prediction for Social Graphs Based on Louvain Algorithm

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