The Role of Community Detection Methods in Performance Variations of Graph Mining Tasks

In real-world scenarios, large graphs represent relationships among entities in complex systems. Mining these large graphs often containing millions of nodes and edges helps uncover structural patterns and meaningful insights. Dividing a large graph into smaller subgraphs facilitates complex system analysis by revealing local information. Community detection extracts clusters or communities of graphs based on statistical methods and machine learning models using various optimization techniques. Structure based community detection methods are more suitable for applying to graphs because they do not rely heavily on rich node or edge attribute information. The features derived from these communities can improve downstream graph mining tasks, such as link prediction and node classification. In real-world applications, we often lack ground truth community information. Additionally, there is neither a universally accepted gold standard for community detection nor a single method that is consistently optimal across diverse applications. In many cases, it is unclear how practitioners select community detection methods, and choices are often made without explicitly considering their potential impact on downstream tasks. In this study, we investigate whether the choice of community detection algorithm significantly influences the performance of downstream applications. We propose a framework capable of integrating various community detection methods to systematically evaluate their effects on downstream task outcomes. Our comparative analysis reveals that specific community detection algorithms yield superior results in certain applications, highlighting that method selection substantially affects performance.

A Survey on Signed Graph Embedding: Methods and Applications

A signed graph (SG) is a graph where edges carry sign information attached to it. The sign of a network can be positive, negative, or neutral. A signed network is ubiquitous in a real-world network like social networks, citation networks, and various technical networks. There are many network embedding models have been proposed and developed for signed networks for both homogeneous and heterogeneous types. SG embedding learns low-dimensional vector representations for nodes of a network, which helps to do many network analysis tasks such as link prediction, node classification, and community detection. In this survey, we perform a comprehensive study of SG embedding methods and applications. We introduce here the basic theories and methods of SGs and survey the current state of the art of signed graph embedding methods. In addition, we explore the applications of different types of SG embedding methods in real-world scenarios. As an application, we have explored the citation network to analyze authorship networks. We also provide source code and datasets to give future direction. Lastly, we explore the challenges of SG embedding and forecast various future research directions in this field.