A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

Sun, Hanlin; Jie, Wei; Sauer, Christian; Ma, Songde; Han, Gang; Wang, Lizhe; Xing, Kui

doi:10.1016/j.future.2018.05.071

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…For example, refs. [19][20][21] all use a min-cut method to split the graph, but the way in which they recombine the graphs is different. When splitting the graph, reference [19] creates copies of nodes that have edges connected to nodes in other subgraphs, and those nodes are simply combined together when the algorithm is finished.…”

Section: Current Parallel Methodsmentioning

confidence: 99%

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Anderson,

Potikas,

Potika

2023

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Community detection has been (and remains) a very important topic in several fields. From marketing and social networking to biological studies, community detection plays a key role in advancing research in many different fields. Research on this topic originally looked at classifying nodes into discrete communities (non-overlapping communities) but eventually moved forward to placing nodes in multiple communities (overlapping communities). Unfortunately, community detection has always been a time-inefficient process, and datasets are too large to realistically process them using traditional methods. Because of this, recent methods have turned to parallelism and graph stream models, where the edge list is accessed one edge at a time. However, all these methods, while offering a significant decrease in processing time, still have several shortcomings. We propose a new parallel algorithm called community detection with seed sets (CoDiS), which solves the overlapping community detection problem in graph streams. Initially, some nodes (seed sets) have known community structures, and the aim is to expand these communities by processing one edge at a time. The innovation of our approach is that it splits communities among the parallel computation workers so that each worker is only updating a subset of all the communities. By doing so, we decrease the edge processing throughput and decrease the amount of time each worker spends on each edge. Crucially, we remove the need for every worker to have access to every community. Experimental results show that we are able to gain a significant improvement in running time with no loss of accuracy.

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Section: Current Parallel Methodsmentioning

confidence: 99%

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Anderson,

Potikas,

Potika

2023

Information

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“…In Sun et al (2018), researchers used swarm intelligence for community detection, which is a task of critical value in the analysis of complex networks. This is especially important for dynamic networks, where the properties of decentralized, self-organized, and self-evolving systems are of importance.…”

Section: Use Of Swarm Intelligence In the Contextmentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

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Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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“…Lyu et al [ 40 ] propose a novel local community detection method called evolutionary-based local community detection (ECLD), which utilizes the entire obtained information and PSO algorithm to find the local community structures in the complex networks. Sun et al [ 55 ] introduce a Parallel Self-organizing Overlapping Community Detection (PSOCD) method inspired by the swarm intelligence system to detect the overlapping communities in the large scale dynamic complex networks. It treats the complex networks as a decentralized, self-organized, and self-evolving system.…”

Section: Real-world Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

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The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.

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A parallel self-organizing overlapping community detection algorithm based on swarm intelligence for large scale complex networks

Cited by 16 publications

References 40 publications

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

CoDiS: Community Detection via Distributed Seed Set Expansion on Graph Streams

Computational Intelligence in the Context of Industry 4.0

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

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