Clustering attributed graphs: models, measures and methods (1501.01676v1)

Abstract: Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

• The paper presents a comprehensive review of attributed graph clustering, detailing models that integrate node and edge attributes with traditional graph structures.
• The paper demonstrates various clustering methods that enhance community detection by combining structural proximity with attribute similarity using techniques like edge weight modification and multi-dimensional optimization.
• The paper highlights evaluation challenges and future research directions aimed at improving scalability, explainability, and dynamic adaptation in information-rich networks.

Clustering Attributed Graphs: Models, Measures, and Methods

The paper under consideration offers a comprehensive survey of clustering attributed graphs, an area of increasing importance due to its applicability in various domains including social network analysis. Traditional graph clustering has largely focused on unweighted graphs, often excluding node and edge attributes which are vital for a more integrated understanding of real-world networks. This paper by Bothorel, Cruz, Magnani, and Micenkov provides an insightful review of recent advancements in attributed graph models, measures, and clustering methods, offering a unified framework for understanding these complex systems.

Overview of Attributed Graphs

Attributed graphs incorporate additional data in the form of node and edge attributes that go beyond the mere graph structure. These attributes provide richer information about the entities and their relationships, unveiling dimensions such as actor features and types of interactions which traditional unweighted graphs overlook. The integration of node and edge attributes into the clustering process allows for a more nuanced capture of community structures that reflect not only the connection topology but also the contextual similarities among nodes.

Graph Models and Clustering Methods

The paper classifies attributed graph models into several types, each providing a unique perspective on incorporating attributes into the graph structure. These models range from simple extensions that add attribute vectors to nodes, to complex multi-layered models that handle multiple types of edges simultaneously. Such frameworks allow for sophisticated clustering techniques that account for both structural and attribute-driven proximities.

Methods for clustering attributed graphs extend traditional graph clustering algorithms by incorporating attributes into the clustering process. Techniques such as weight modification of edges based on node attribute similarity, the linear combination of structural and compositional dimensions, and random walk models provide flexible frameworks for discovering communities. Even generative models and statistical inference methods make use of latent space approaches that leverage attribute data to enhance clustering accuracy.

Evaluation and Challenges

Evaluating the quality of clusters in attributed graphs presents unique challenges. Traditional measures such as modularity need computational extensions to account for the interplay of attributes and structure. The paper discusses various evaluation metrics, highlighting the importance of multi-objective optimization that balances structural and compositional considerations. Despite advances, several challenges remain, including the computational complexity of multi-dimensional cluster analysis and the management of the high dimensionality of attribute data.

Practical and Theoretical Implications

The integration of attributes in graph clustering holds significant potential for both practical applications and theoretical advancements. It enables the detection of more contextually relevant communities, which is particularly useful in dynamic, information-rich environments like social media and biological networks. Theoretically, this field posits new questions around the balance of multi-type relation analysis and attribute integration, driving future research towards more scalable and adaptable clustering algorithms.

Future Directions

Future developments in attributed graph clustering will likely focus on improving scalability, handling dynamic changes over time, and facilitating interactive and explainable clustering methodologies. The exploration of machine learning models, especially unsupervised and semi-supervised approaches, is expected to contribute significantly to advancing this field. Moreover, bridging gaps between distinct clustering methodologies across different domains could lead to robust, general-purpose clustering frameworks.

In summary, this survey provides a detailed and organized exposition of the current landscape in attributed graph clustering, setting a foundation for continued exploration and innovation in this multifaceted domain. The paper emphasizes the intricate balance required between leveraging additional data dimensions and maintaining computational efficiency, pointing out directions for future research to enhance both the depth and applicability of clustering techniques in complex network analyses.