Community Detection in Networks with Node Attributes

Yang, Jaewon; McAuley, Julian; Leskovec, Jure

doi:10.1109/icdm.2013.167

Cited by 677 publications

(431 citation statements)

References 29 publications

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“…CESNA was introduced by Yang et al to identify Communities from Edge Structure and Node Attributes [35]. One advantage of this method is its ability to detect overlapping communities by modeling the interaction between the network structure and the node attributes.…”

Section: Related Workmentioning

confidence: 99%

I-Louvain: An Attributed Graph Clustering Method

Combe

Largeron

Géry

et al. 2015

Lecture Notes in Computer Science

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Abstract. Modularity allows to estimate the quality of a partition into communities of a graph composed of highly inter-connected vertices. In this article, we introduce a complementary measure, based on inertia, and specially conceived to evaluate the quality of a partition based on real attributes describing the vertices. We propose also I-Louvain, a graph nodes clustering method which uses our criterion, combined with Newman's modularity, in order to detect communities in attributed graph where real attributes are associated with the vertices. Our experiments show that combining the relational information with the attributes allows to detect the communities more efficiently than using only one type of information. In addition, our method is more robust to data degradation.

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Section: Related Workmentioning

confidence: 99%

I-Louvain: An Attributed Graph Clustering Method

Combe

Largeron

Géry

et al. 2015

Lecture Notes in Computer Science

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“…Yang et al [17] have introduced communities from edge structure and node attributes (CESNA). This network size has a linear runtime so it is processed with high magnitude while compared with other approaches.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

Singh¹,

Singh²

2018

IJIES

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Abstract:The complex networks are offering a high resource of heterogeneous data and the proper and efficient analysis discovers the unknown information and relations in networks. Due to the huge number of users and nonfamiliar fraud detection system in complex networks, a lot of online frauds introduce to affects the networks. In this paper, we concentrate on both community and fraud detection to minimize the link and node failures in the complex networks. A hybrid optimization algorithm proposed for community and fraud detection in the complex networks (HCFD-Net). The first contribution is to detect the community based on fruit fly optimization algorithm with differential evolution (FOADE). The second contribution is that the fraud detection is achieved by contingency table terminology with multi-link metrics. The performance of the HCFD-Net is analyzed on different five real-world networks are Zachary's karate club, Bottlenose dolphins', American college football, American political books, and Amazon online purchase network. The simulation result shows that the proposed HCFD-NET perform very efficient than existing algorithms in terms of normalized mutual information (NMI) and network lifetime.

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“…This type of approaches provides a more conceptual partition of the network that is not necessarily proportional to context. Of clustering methods SA-Cluster [4] and CESNA [17] can be cited.…”

Section: Link Based Clustering (Also Known As Communitymentioning

confidence: 99%

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

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Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. Basic methods in the field used the criteria coming from graph theory, but new approaches are trying to take advantage of the realm of traditional data exploration and linked data. One of these new approaches is the approach called conceptual link approach, which was introduced to describe the social networks. In this approach, using the concept of contextual links, knowledge of the social network through a conceptual view of the dramatic structure is summarized as means of social networking. Conceptual perspective provides a summary of existing knowledge on a social network.

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Community Detection in Networks with Node Attributes

Cited by 677 publications

References 29 publications

I-Louvain: An Attributed Graph Clustering Method

I-Louvain: An Attributed Graph Clustering Method

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

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