Towards Online Multiresolution Community Detection in Large-Scale Networks

Huang, Jianbin; Sun, Hui; Liu, Yaguang; Song, Qinbao; Weninger, Tim

doi:10.1371/journal.pone.0023829

Cited by 106 publications

(82 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This function is thus criterion dependent and its implementation is not fixed. In our work, for the criterion from Huang et al we followed the growth method from the authors [10]. However for Lancichinetti et al's criterion we wrote a new growth method (see implementation details in Algorithm 4) as the one described in [9] has a high computational complexity.…”

Section: Algorithm For Local Criteriamentioning

confidence: 99%

“…The work by [10] uses an efficient algorithm. The authors report for a single scale a speed of 650 s on a network with 5.10 5 nodes and 5.10 6 edges.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…Several criteria designed for multi-scale analysis have been presented in [6,7,8,9,10]. However no efficient method to uncover communities across scales was suggested.…”

Section: Introductionmentioning

confidence: 99%

“…Large values of α lead to small communities while small values lead to large ones. Another approach presented in [10] uses the structural similarity between nodes given by…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Martelot

Hankin

2013

The Computer Journal

View full text Add to dashboard Cite

Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organisation, or scales, a network may encompass and are therefore limited. In this paper we present a method compatible with global and local criteria that enables fast multi-scale community detection on large networks. The method is derived in two algorithms, one for each type of criterion, and implemented with 6 known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches. In particular our results suggest that global criteria seem to be more robust to noise and thus more accurate than local criteria.

show abstract

Section: Algorithm For Local Criteriamentioning

confidence: 99%

“…The work by [10] uses an efficient algorithm. The authors report for a single scale a speed of 650 s on a network with 5.10 5 nodes and 5.10 6 edges.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…Several criteria designed for multi-scale analysis have been presented in [6,7,8,9,10]. However no efficient method to uncover communities across scales was suggested.…”

Section: Introductionmentioning

confidence: 99%

“…Large values of α lead to small communities while small values lead to large ones. Another approach presented in [10] uses the structural similarity between nodes given by…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Martelot

Hankin

2013

The Computer Journal

View full text Add to dashboard Cite

show abstract

“…After a set number of iterations, LPA terminates and the labels of the nodes of the network are returned [1]. Many other methods have been developed as well, such as Speaker-listener LPA (SLPA) [2], LabelRank [3], and Local Tightness Expansion (LTE) [4].…”

Section: Introductionmentioning

confidence: 99%

Determining Leaders and Communities on Networks Using Neighborhood Similarity

Valyou¹,

Dickinson²,

2014

View full text Add to dashboard Cite

Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety of applications. One of the important topics of network analysis is finding influential nodes. These nodes are of two kinds -leader nodes and bridge nodes. In this study, we propose an algorithm to find strong leaders in a network based on a revision of neighborhood similarity. This leadership detection is combined with a neighborhood intersection clustering algorithm to produce high quality communities for various networks. We also delve into the structure of a new network, the Houghton College Twitter network, and examine the discovered leaders and their respective followers in more depth than which is frequently attempted for a network of its size. The results of the observations on this and other networks demonstrate that the community partitions found by this algorithm are very similar to those of ground truth communities.

show abstract

Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network

Zheng,

You,

Lam

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

Hydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short‐term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems.

show abstract

Towards Online Multiresolution Community Detection in Large-Scale Networks

Cited by 106 publications

References 30 publications

Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks

Determining Leaders and Communities on Networks Using Neighborhood Similarity

Fracture Prediction of Hydrogel Using Machine Learning and Inhomogeneous Multiscale Network

Contact Info

Product

Resources

About