NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach [PDF]
Sensors, 2019 With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are
Bingyang Huang +2 more
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Co-Association Matrix-Based Multi-Layer Fusion for Community Detection in Attributed Networks [PDF]
Entropy, 2019 Community detection is a challenging task in attributed networks, due to the data inconsistency between network topological structure and node attributes.
Sheng Luo +3 more
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Coupled Node Similarity Learning for Community Detection in Attributed Networks [PDF]
Entropy, 2018 Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important.
Fanrong Meng +4 more
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Identifying vital nodes for influence maximization in attributed networks [PDF]
Scientific Reports, 2022 Identifying a set of vital nodes to achieve influence maximization is a topic of general interest in network science. Many algorithms have been proposed to solve the influence maximization problem in complex networks.
Ying Wang, Yunan Zheng, Yiguang Liu
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Generating attributed networks with communities. [PDF]
PLoS ONE, 2015 In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally
Christine Largeron +3 more
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Anchor Link Prediction across Attributed Networks via Network Embedding [PDF]
Entropy, 2019 Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation,
Shaokai Wang +6 more
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Dual contrastive learning-based reconstruction for anomaly detection in attributed networks. [PDF]
PLoS ONEAnomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains.
Hossein Rafieizadeh +3 more
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IEEE Access, 2021 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 ...
Hedia Zardi +4 more
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Network Representation Learning With Community Awareness and Its Applications in Brain Networks
Frontiers in Physiology, 2022 Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes.
Min Shi, Bo Qu, Xiang Li, Cong Li
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Community Discovery Algorithm for Attributed Networks Based on Bipartite Graph Representation [PDF]
Jisuanji kexue, 2023 Community discovery in attributed networks is an important research content in network data analysis.To improve the accuracy of community discovery,most existing algorithms perform low-dimensional representation of attributed networks by fusing ...
ZHAO Xingwang, XUE Jinfang
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