Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare, Hadi; Hajiabadi, Mahdi; Jalili, Mahdi

doi:10.1109/tkde.2019.2960222

Cited by 15 publications

(7 citation statements)

References 49 publications

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“…NMI is used to measure the accuracy of the proposed method using mutual information of similarity (or dissimilarity) compared to the groundtruth method [25]. The NMI is defined in Eq.…”

Section: Experiments Scenariomentioning

confidence: 99%

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

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The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN.

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“…NMI is used to measure the accuracy of the proposed method using mutual information of similarity (or dissimilarity) compared to the groundtruth method [25]. The NMI is defined in Eq.…”

Section: Experiments Scenariomentioning

confidence: 99%

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

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“…When the nodes are described with a set of attributes, the network is said attributed. In this case, it is crucial to consider both the network structure and the members' properties to have densely connected members within the communities, which share common attributes [36], [38]. A few recent studies have considered attributed networks.…”

Section: ) Community Detection In 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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“…An appropriate method among path-based algorithms that is recommended in sparse graphs is the SRW 1 method, which improves the results probably. One can attempt to experiment newer and better community detection algorithms for higher precision, such as [27] or [28]. Moreover, a mechanism has been sought to utilize weighted graph version of the network for improvement of the results using inter-cluster relations and their outcomes.…”

Section: -Conclusion and Future Workmentioning

confidence: 99%

Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection

Salvati¹,

Mohasefi²,

Sulaimany³

2021

JIST

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Link prediction seeks to detect missing links and the ones that may be established in the future given the network structure or node features. Numerous methods have been presented for improving the basic unsupervised neighbourhood-based methods of link prediction. A major issue confronted by all these methods, is that many of the available networks are sparse. This results in high volume of computation, longer processing times, more memory requirements, and more poor results. This research has presented a new, distinct method for link prediction based on community detection in large-scale sparse networks. Here, the communities over the network are first identified, and the link prediction operations are then performed within each obtained community using neighbourhood-based methods. Next, a new method for link prediction has been carried out between the clusters with a specified manner for maximal utilization of the network capacity. Utilized community detection algorithms are Best partition, Link community, Info map and Girvan-Newman, and the datasets used in experiments are Email, HEP, REL, Wikivote, Word and PPI. For evaluation of the proposed method, three measures have been used: precision, computation time and AUC. The results obtained over different datasets demonstrate that extra calculations have been prevented, and precision has been increased. In this method, runtime has also been reduced considerably. Moreover, in many cases Best partition community detection method has good results compared to other community detection algorithms.

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Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Cited by 15 publications

References 49 publications

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

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

Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection

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