Community Detection Based on DeepWalk in Large Scale Networks

Chen, Yunfang; Wang, Li; Qi, Dehao; Zhang, Wei

doi:10.1007/978-981-15-7530-3_43

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Further, the technology adopted does not rely on big data analysis technology. However, the large scale and complex structures of real networks [25] make it difficult to identity key information and talk patterns in a large-scale corpus. Relevant studies of "YiXi" are limited to a qualitative perspective, such as demonstrating the innovation and improvement measures, communication characteristics of new media [26], and program style and dissemination effect [27].…”

Section: Analysis For Talk Transcriptsmentioning

confidence: 99%

Key Information Extraction and Talk Pattern Analysis Based on Big Data Technology: A Case Study on YiXi Talks

Jiang

Chuan-feng

et al. 2022

Electronics

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In the attempt to extract key information and talk patterns from YiXi talks in China to realize “strategic reading” for readers and newcomers of the speaking field, text mining methods are used by this work. The extraction of key information is realized by keyword extraction using the TF-IDF algorithm to show key information of one talk or one category of talks. Talk pattern recognition is realized by manual labeling (100 transcripts) and rule-based automatic programs (590 transcripts). The labeling accuracy rate of “main narrative angle” recognition is the highest (70.34%), followed by “opening form” (65.25%) and “main narrative object”, and the “ending form” is around 50%, with the overall accuracy of the rule-based automatic recognition program for talk patterns at approximately 60%. The obtained results show that the proposed keyword extraction technology for transcripts can provide “strategic reading” to a certain extent. Mature speech mode can be summarized as follows: speakers tend to adopt a self-introducing opening format. They tell stories and experiences through a first-person narrative angle and express expectations and prospects for the future. This pattern is reasonable and can be referenced by new speakers.

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Section: Analysis For Talk Transcriptsmentioning

confidence: 99%

Key Information Extraction and Talk Pattern Analysis Based on Big Data Technology: A Case Study on YiXi Talks

Jiang

Chuan-feng

et al. 2022

Electronics

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“…An intuitive way of community detection based on network representation is to obtain the nodes embedding of the network by applying some kind of graph embedding model, and then cluster the embeddings by a clustering algorithm [4] [5], so as to achieve the goal of community detection. However, in such approach, the network representation process is independent of the node clustering process, and the network representation model cannot get feedback from the nodes clustering model.…”

Section: Graph Embedding and Community Detectionmentioning

confidence: 99%

“…Graph embedding can provide effective input for downstream machine learning tasks, such as node classification [1], link prediction [2] and graph visualization [3]. With the gradual maturity of graph embedding, some scholars try to apply it to community detection tasks [4] [5]. However, most existing graph embedding models are not designed for community detection, so they may not be able to effectively detect the community structures in networks.…”

Section: Introductionmentioning

confidence: 99%

Graph Embedding Models for Community Detection

Chen¹,

Gao²,

Liu³

2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Graph embedding models, also known as network representation models, have been tried to be applied to community detection tasks. However, most existing graph embedding models are not specially designed for community detection tasks and thus may be incapable of revealing the community structures in networks well. To fill this gap, this paper proposes two novel graph embedding models, GEMod and GEMap, which are specially designed for community detection. The proposed methods try to optimize the modified modularity and two-level coding length while learning the nodes embedding, so that the learned nodes embedding can be better applied to detect community structures in networks. Experimental results show that the algorithms proposed are superior or comparable to other community detection algorithms based on graph embedding models. Besides, the nodes embedding generated by GEMod and GEMap are generally more compact and separable, which means that they are more suitable for clustering tasks.

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“…Generally, k-means is applied to the learned latent vector with giving a priori. A Gaussian mixture model empowered with Bayesian can provide insights in terms of uncertainty and exploit nonlinearities which result in better performance in comparison to state-of-the-art community detection methods [30]. Additionally, Bayesian inference solves the problem where the number of communities is uncertain [2], uses variational inference for community detection, and obtains a good result, which first convolves on the network, learning structural and nodal features in the process.…”

Section: Related Workmentioning

confidence: 99%

Community Detection Based on DeepWalk Model in Large-Scale Networks

Chen

Wang

et al. 2020

Security and Communication Networks

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The large-scale and complex structure of real networks brings enormous challenges to traditional community detection methods. In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. Firstly, in order to solve the scarce problem of network data, this paper uses the DeepWalk model to embed a high-dimensional network into low-dimensional space with topology information. Then, low-dimensional data are processed, with each node treated as a sample and each dimension of the node as a feature. Finally, samples are fed into a Gaussian mixture model (GMM), and in order to automatically learn the number of communities, variational inference is introduced into GMM. Experimental results on the DBLP dataset show that the model method of this paper can more effectively discover the communities in large-scale networks. By further analyzing the excavated community structure, the organizational characteristics within the community are better revealed.

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Community Detection Based on DeepWalk in Large Scale Networks

Cited by 3 publications

References 16 publications

Key Information Extraction and Talk Pattern Analysis Based on Big Data Technology: A Case Study on YiXi Talks

Key Information Extraction and Talk Pattern Analysis Based on Big Data Technology: A Case Study on YiXi Talks

Graph Embedding Models for Community Detection

Community Detection Based on DeepWalk Model in Large-Scale Networks

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