Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks

Evolution of Communities with Focus on Stability

Community detection is an important tool for analyzing the social graph of mobile phone users. The problem of finding communities in static graphs has been widely studied. However, since mobile social networks evolve over time, static graph algorithms are not sufficient. To be useful in practice (e.g. when used by a telecom analyst), the stability of the partitions becomes critical. We tackle this particular use case in this paper: tracking evolution of communities in dynamic scenarios with focus on stability. We propose two modifications to a widely used static community detection algorithm: we introduce fixed nodes and preferential attachment to pre-existing communities. We then describe experiments to study the stability and quality of the resulting partitions on real-world social networks, represented by monthly call graphs for millions of subscribers.Comment: AST at 42nd JAIIO, September 16-20, 2013, Cordoba, Argentina. arXiv admin note: substantial text overlap with arXiv:1311.550

Temporal analysis of honey bee interaction networks based on spatial proximity

The BeesBook system provides high-resolution data about bee movements within a single colony by automatically tracking individual honey bees inside a hive over their entire life. This thesis focuses on the process of designing and implementing a network pipeline to extract interaction networks from this data. Spatial proximity is used as an indicator for interactions between bees. Social network analysis methods were applied to investigate the static and dynamic properties of the resulting social networks of honey bees on a global, intermediate and local level. The resulting networks were characterized by a low hierarchical structure and a high density. The global structure of the colony seems to be stable over time. The local structure is highly dynamic, as bees change communities as they age. Communities in the honey bee network are formed by age groups that show a high spatial fidelity. The findings are in line with the established state of research that colonies are organized around age-based task division. The results of the analysis validate the implemented pipeline and the inferred networks. Consequently, this work provides an excellent foundation for future research focusing on temporal network analysis

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks