Overlapping Community Detection Based on Attribute Augmented Graph

Lin, Hanyang; Zhan, Yongzhao; Zhao, Zhongqiu; Chen, Yuzhong; Dong, Chen

doi:10.3390/e23060680

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…The latent representation, denoted as 𝑍, is obtained from the GCN encoder. The goal of GAEs is to capture the inherent relationships and dependencies within the graph, allowing for meaningful analysis and prediction tasks [33,34].…”

Section: Graph Convolutional Network (Gcns) Are a Variant Of Convolut...mentioning

confidence: 99%

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

Besharatifard,

Vafaee

2023

Preprint

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Combinational therapies with synergistic effects provide a powerful treatment strategy for tackling complex diseases, particularly malignancies. Discovering these synergistic combinations, often involving various compounds and structures, necessitates exploring a vast array of compound pairings. However, practical constraints such as cost, feasibility, and complexity hinder exhaustive in vivo and in vitro experimentation. In recent years, machine learning methods have made significant inroads in pharmacology. Among these, Graph Neural Networks (GNNs) have gained increasing attention in drug discovery due to their ability to represent complex molecular structures as networks, capture vital structural information, and seamlessly handle diverse data types. This review aims to provide a comprehensive overview of various GNN models developed for predicting effective drug combinations, examining the limitations and strengths of different models, and comparing their predictive performance. Additionally, we discuss the databases used for drug synergism prediction and the extraction of drug-related information as predictive features. By summarizing the state-of-the-art GNN-driven drug combination prediction, this review aims to offer valuable insights into the promising field of computational pharmacotherapy.

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Section: Graph Convolutional Network (Gcns) Are a Variant Of Convolut...mentioning

confidence: 99%

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

Besharatifard,

Vafaee

2023

Preprint

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“…Li X et al [31] proposed algorithm combines local expansion and label propagation to detect overlapping components in community structures, achieving higher effectiveness, accuracy, and stability across diverse network structures. Lin H et al [32] proposed an algorithm for detecting overlapping communities in social networks using an augmented attribute graph and an improved weight adjustment strategy, demonstrating its effectiveness through extensive experiments on synthetic and real-world datasets. Huang M et al [33] proposed approach, ESNMF, utilizes symmetric non-negative matrix factorization and outperforms existing methods for community detection in large-scale networks by dividing the network into sub-graphs and extracting precise communities through non-negative matrix factorization.…”

Section: Overlapping Community Detectionmentioning

confidence: 99%

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

et al. 2023

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Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks.

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