A Comprehensive Survey on Community Detection with Deep Learning (2105.12584v2)

Abstract: A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to academics and practitioners. This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

• The paper presents a taxonomy of deep learning methods that advances traditional community detection techniques.
• The study categorizes approaches like CNNs, GCNs, GATs, GANs, and autoencoders to handle both disjoint and overlapping communities.
• It highlights challenges in scaling to dynamic, large, and heterogeneous networks, paving the way for future research directions.

Overview of "A Comprehensive Survey on Community Detection with Deep Learning"

The paper "A Comprehensive Survey on Community Detection with Deep Learning," presented in IEEE Transactions on Neural Networks and Learning Systems, provides a thorough exploration of recent advancements in community detection techniques through the application of deep learning methodologies. Highlighting the transition from classical techniques such as spectral clustering and statistical inference to innovative deep learning strategies, the paper lays the groundwork for understanding the state-of-the-art in tackling community detection problems across various domains.

Community detection in networks, which involves identifying groups of nodes more densely connected internally than with the rest of the network, is critical for insights into social contexts, biological systems, information organization, and beyond. This survey meticulously categorizes deep learning methodologies into various classes, emphasizing their ability to process complex, high-dimensional data in a way that traditional approaches might not handle effectively.

The authors introduce a taxonomy that distinguishes between different deep learning paradigms, including convolutional neural networks (CNNs), graph convolutional networks (GCNs), graph attention networks (GATs), generative adversarial networks (GANs), autoencoders (AEs), and nonnegative matrix factorization (NMF). Furthermore, this taxonomy accounts for the application of deep sparse filtering (DSF) and evaluates the impact of these methods on both disjoint and overlapping community detection.

Methodological Advancements

1. Convolutional Networks: The paper discusses CNNs and GCNs as foundational techniques that facilitate learning on grid-like and non-grid structures, respectively. GCNs are particularly notable for incorporating neighborhood information to improve representation quality in network structures.
2. Graph Attention Networks (GATs): GATs employ attention mechanisms to manage and weigh node influence within their neighborhoods, which is particularly useful in capturing community defects under varying network types, including multiplex and heterogeneous networks.
3. Generative Adversarial Networks (GANs): The implementation of GANs in community detection reflects their utility in generating adversarial samples, thereby enhancing model robustness against overfitting while preserving rich semantic and structural properties.
4. Autoencoders (AEs): With AE and VAE variants, this approach excels in unsupervised learning by reconstructing and representing features from high-dimensional data, addressing issues like sparsity and noise effectively.
5. Deep Nonnegative Matrix Factorization (DNMF): This method extends NMF’s utility to deeper, layered contexts to better model community structures in the network embedding task.
6. Deep Sparse Filtering (DSF): DSF offers a method to discover communities through feature learning that optimizes the sparsity of the representation.

Implications and Challenges

This survey compellingly underscores the considerable advantages deep learning approaches offer over traditional methods in rendering community detection more adaptable and efficient. However, certain challenges persist—specifically, handling large-scale networks, addressing dynamic network conditions, and ensuring robust performance in the face of incomplete network topology.

Practically, the developments surveyed suggest promising applications across a broad range of domains, including social network analysis, bioinformatics, and recommendation systems. Addressing challenges such as unknown community numbers, multi-layer networks, and leveraging cross-domain knowledge requires continued innovation.

Future Directions

The paper closes by identifying several open research avenues. Future exploration may focus on enhancing community embedding techniques, refining methods for dynamic and multi-layer networks, and advancing strategies for handling network heterogeneity and heterophily. Additionally, the integration of explainability and interpretability into community detection models remains a ripe field for exploration.

In summary, this survey provides a cornerstone reference for researchers and practitioners aiming to harness deep learning techniques in community detection, framing both the current landscape and potential growth trajectories within this vibrant research area.