Mining application-aware community organization with expanded feature subspaces from concerned attributes in social networks

Wu, Peng; Pan, Li

doi:10.1016/j.knosys.2017.10.007

Cited by 20 publications

(7 citation statements)

References 38 publications

(65 reference statements)

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“…The attribute proximity and structural proximity between nodes are the basis of many network analysis tasks. For example, community detection on social networks clusters nodes based on the structural proximity and attribute proximity [37]. In recommendation on citation networks, papers having strong structural and attribute proximity are most likely to be reference papers of the given manuscript [38].…”

Section: Multimodal Deep Network Embedding Methods a Problem Definitionmentioning

confidence: 99%

Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

Zheng

Pan

2020

IEEE Trans. Neural Netw. Learning Syst.

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Network embedding is the process of learning lowdimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providing extra semantic information. It has been demonstrated that both structural and attribute features are important for network analysis tasks. To preserve both features, we investigate the problem of integrating structure and attribute information to perform network embedding and propose a Multimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linear network structures and the complex interactions among structures and attributes, using a deep model consisting of multiple layers of non-linear functions. Since structures and attributes are two different types of information, a multimodal learning method is adopted to pre-process them and help the model to better capture the correlations between node structure and attribute information. We employ both structural proximity and attribute proximity in the loss function to preserve the respective features and the representations are obtained by minimizing the loss function. Results of extensive experiments on four real-world datasets show that the proposed method performs significantly better than baselines on a variety of tasks, which demonstrate the effectiveness and generality of our method.

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Section: Multimodal Deep Network Embedding Methods a Problem Definitionmentioning

confidence: 99%

Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

Zheng

Pan

2020

IEEE Trans. Neural Netw. Learning Syst.

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“…The research and application of attributed networks are extensive. For example, it is used to detect social groups built up by interest 18 or temporary social groups built up by common features 19 in social networks. In IoT networks, it is used to predict network quality of service based on contextual information clustering and deep latent feature learning, 20 provide resource allocation services, 21 and construct a systematic approach to model both entity and relationship elements.…”

Section: Related Workmentioning

confidence: 99%

“…DBCSC also needs to establish numerous parameters in advance, and the performance on some data sets is not as good as GAMer. Wu and Pan 18 proposed the ACM algorithm to mine an application-aware community organization with respect to a set of specific concerned attributes. ACM constructs a network backbone composed of nodes with similar concerned attributes.…”

Section: Related Workmentioning

confidence: 99%

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

Jia

et al. 2020

Computational Intelligence

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Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X-means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X-means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real-attributed and synthetic-attributed networks show 484

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“…However, most of real-world social networks present more information about social network nodes than just the existing relationships between them. For instance, in several social network graphs, it is rather common that certain nodes' attributes such as age, gender, interests, are available as part of a-priori knowledge [17][18][19]. For instance, one may find in Facebook network graph, both information concerning the communication between the different users and the profile of these users, which may include gender, age and location.…”

Section: Introductionmentioning

confidence: 99%

A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

Rostami¹,

Oussalah²

2021

Preprint

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Community detection is one of the basic problems in social network analysis. Community detection on an attributed social networks aims to discover communities that have not only adhesive structure but also homogeneous node properties. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, a novel attributed community detection method through an integration of feature weighting with node centrality techniques is developed in this paper. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state of the art methods and ascertain the effectiveness of the developed method for attributed community detection.

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Mining application-aware community organization with expanded feature subspaces from concerned attributes in social networks

Cited by 20 publications

References 38 publications

Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

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