82,896 research outputs found
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
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
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
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