A fast and efficient incremental approach toward dynamic community detection

Zarayeneh, Neda; Kalyanaraman, Ananth

doi:10.1145/3341161.3342877

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Table 11: Some weaknesses and strengths of incremental methods based on modularity optimization, density, and label propagation [52,30,40,63,4,46,18,10,70,16,55,6,68,47,54,42,36,8,57,65] 5.4 Advantages and disadvantages of incremental methods for community detection in both fully and growing dynamic networks…”

Section: Methods Based On Weaknesses Strengthsmentioning

confidence: 99%

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Incremental methods for community detection in both fully and growing dynamic networks

Bouhatem

Hadj

Souam

et al. 2021

Acta Universitatis Sapientiae, Informatica

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In recent years, community detection in dynamic networks has received great interest. Due to its importance, many surveys have been suggested. In these surveys, the authors present and detail a number of methods that identify a community without taking into account the incremental methods which, in turn, also take an important place in dynamic community detection methods. In this survey, we provide a review of incremental approaches to community detection in both fully and growing dynamic networks. To do this, we have classified the methods according to the type of network. For each type of network, we describe three main approaches: the first one is based on modularity optimization; the second is based on density; finally, the third is based on label propagation. For each method, we list the studies available in the literature and state their drawbacks and advantages.

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Section: Methods Based On Weaknesses Strengthsmentioning

confidence: 99%

“…Incremental community detection methods in growing dynamic networks based on modularity optimization[68,54,65] …”

mentioning

confidence: 99%

Incremental methods for community detection in both fully and growing dynamic networks

Bouhatem

Hadj

Souam

et al. 2021

Acta Universitatis Sapientiae, Informatica

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“…We demonstrate in this paper, the utility of this technique using the modularity-optimization function. A preliminary version of the method proposed in this paper appeared in [48]. That preliminary version of the method only addressed edge additions.…”

Section: Related Workmentioning

confidence: 99%

Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs

Zarayeneh

Kalyanaraman

2021

IEEE Trans. Netw. Sci. Eng.

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Detecting communities in time-evolving/dynamic networks is an important operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. In this paper, we present a new technique called Delta-Screening (or simply, ∆-screening) for updating communities in a dynamic graph. The technique assumes that the graph is given as a series of time steps, and outputs a set of communities for each time step. At the start of each time step, the ∆-screening technique examines all changes (edge additions and deletions) and computes a subset of vertices that are likely to be impacted by the change (using the modularity objective). Subsequently, only the identified subsets are processed for community state updates. Our experiments demonstrate that this scheme, despite its ability to prune vertices aggressively, is able to generate significant savings in runtime performance (up to 38× speedup over static baseline and 5× over dynamic baseline implementations), without compromising on the quality. We test on both real-world and synthetic network inputs containing both edge additions and deletions. The ∆-screening technique is generic to be incorporated into any of the existing modularity-optimizing clustering algorithms. We tested using two state-of-the-art clustering implementations, namely, Louvain and SLM. In addition, we also show how to use the ∆-screening approach to delineate appropriate intervals of temporal resolutions at which to analyze a given input network.

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“…In [33], the authors proposed a filtering technique called δ-screening which can be combined with any community detection algorithm that uses modularity as an objective function. δ-Screening allows to speedily locate the parts of the network that are probably affected by the latest events in the network.…”

Section: ) Community Detection In Non-attributed Networkmentioning

confidence: 99%

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

et al. 2021

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Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities' transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

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A fast and efficient incremental approach toward dynamic community detection

Cited by 17 publications

References 23 publications

Incremental methods for community detection in both fully and growing dynamic networks

Incremental methods for community detection in both fully and growing dynamic networks

Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

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