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Community detection in attributed networks using neighborhood information
Community detection is a crucial aspect in network analysis. Real-world networks are often enriched with attributes providing extensive information...
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Community detection in attributed networks via adaptive deep nonnegative matrix factorization
Community detection plays an important role in analyzing attributed networks. It attempts to find the optimal cluster structures to identify valuable...
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Multi-level self-adaptive prototypical networks for few-shot node classification on attributed networks
Attributed networks, such as social networks, citation networks, and traffic networks, are ubiquitous nowadays. Node classification is an essential...
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An efficient framework for anomaly detection in attributed social networks
Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact...
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Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks
Community detection on attributed networks is a method to discover community structures within attributed networks. By applying community detection...
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Co-MLHAN: contrastive learning for multilayer heterogeneous attributed networks
Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node...
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Indexing complex networks for fast attributed kNN queries
The k nearest neighbor ( k NN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified...
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Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks
Link prediction is one of the most widely studied problems in the area of complex network analysis, in which machine learning techniques can be...
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A multi-view clustering algorithm for attributed weighted multi-edge directed networks
Graph clustering acts as a critical topic for solving decision situations in networks. Different node clustering methods for undirected and directed...
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Dual Contrastive Learning for Anomaly Detection in Attributed Networks
Anomaly detection in attributed networks has been crucial in many critical domains and has gained significant attention in recent years. However,...
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Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex...
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Enhancing Network Role Modeling: Introducing Attributed Multiplex Structural Role Embedding for Complex Networks
Numerous studies have focused on defining node roles within networks, producing network embeddings that maintain the structural role proximity of...
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Link predictability classes in large node-attributed networks
In this paper, we study how the observed quality of a chosen feature-based link prediction model applied to a part of a large node-attributed network...
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Parallel and Distributed Query Processing in Attributed Networks
Graph Analytics (GA) is a rapidly growing field that holds significant importance in numerous applications that focuses on analysing the complex...
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An autoencoder considering multi-order and structural-role similarity for community detection in attributed networks
A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product...
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Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks
Anomaly detection on attributed networks is a vital task in graph data mining and has been widely applied in many real-world scenarios. Despite the...
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Discovering a cohesive football team through players’ attributed collaboration networks
The process of team composition in multiplayer sports such as football has been a main area of interest within the field of the science of teamwork,...
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Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks
Fraud transaction detection is a pressing need in industrial applications, aiming to detect the fraud for a transaction involving the buyer and the...
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Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks
A number of approaches for anomaly detection on attributed networks have been proposed. However, most of them suffer from two major limitations: (1)...
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Adversarial enhanced attributed network embedding
Attributed network embedding aims to extract latent features of complex networks from structural topology and node attributes. Existing embedding...
