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Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning
Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing …
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HGN2T: A Simple but Plug-and-Play Framework Extending HGNNs on Heterogeneous Temporal Graphs
Heterogeneous graphs (HGs) with multiple entity and relation types are common in real-world networks. Heterogeneous graph neural …
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Fine-tuned Personality Federated Learning for Graph Data
Federated Learning (FL) empowers multiple clients to collaboratively learn a global generalization model without the need to share …
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Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
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Generative Evolutionary Anomaly Detection in Dynamic Networks
Anomaly detection in dynamic networks aims to find network elements (e.g., nodes, edges, subgraphs, change points) with significantly …
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Exploring Temporal Community Structure via Network Embedding
Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real …
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HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level
A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies …
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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal …
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Representation Learning on Heterostructures via Heterogeneous Anonymous Walks
Capturing structural similarity has been a hot topic in the field of network embedding (NE) recently due to its great help in …
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Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism
Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the …
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