We propose an automated and unsupervised methodology for a novel summarization of group behavior based on content preference. We show that graph theoretical community evolution (based on similarity of user preference for content) is effective in indexing these dynamics. Combined with text analysis that targets automatically-identified representative content for each community, our method produces a novel multi-layered representation of evolving group behavior. We demonstrate this methodology in the context of political discourse on a social news site with data that spans more than four years and find coexisting political leanings over extended periods and a disruptive external event that lead to a significant reorganization of existing patterns. Finally, where there exists no ground truth, we propose a new evaluation approach by using entropy measures as evidence of coherence along the evolution path of these groups. This methodology is valuable to designers and managers of online forums in need of granular analytics of user activity, as well as to researchers in social and political sciences who wish to extend their inquiries to large-scale data available on the web.
Clustering and community detection methods are in essence network summarization tools @cite_37 @cite_38 @cite_0 @cite_31 @cite_1 . A survey paper by @cite_12 provides a comprehensive summary of this field. Building on this literature, a growing body of work has been produced on community evolution, varying from works on evolutionary clustering @cite_6 @cite_14 @cite_18 and communtiy detection in dynamic social networks @cite_10 to processes that also optimize for smoothness in temporal evolution @cite_32 or use community cores in evolving networks @cite_11 . A categorization and review of community evolution methods is presented by @cite_25 . These works focus solely on the network structure.
@cite_38 @cite_37 @cite_14 @cite_18 @cite_1 @cite_32 @cite_6 @cite_0 @cite_31 @cite_10 @cite_25 @cite_12 @cite_11
2022651954 2479340554 1912249326 144379121
The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method when compared with state-of-the-art approaches. Uncovering community structures of a complex network can help us to understand how the network functions. Over the past few decades, network community detection has attracted growing research interest from many fields. Many community detection methods have been developed. Network community structure detection can be modelled as optimisation problems. Due to their inherent complexity, these problems often cannot be well solved by traditional optimisation methods. For this reason, evolutionary algorithms have been adopted as a major tool for dealing with community detection problems. This paper presents a survey on evolutionary algorithms for network community detection. The evolutionary algorithms in this survey cover both single objective and multiobjective optimisations. The network models involve weighted unweighted, signed unsigned, overlapping non-overlapping and static dynamic ones. Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario. In this paper we present an original approach for community detection in complex networks. The approach belongs to the family of seed-centric algorithms. However, instead of expanding communities around selected seeds as most of existing approaches do, we explore here applying an ensemble clustering approach to different network partitions derived from ego-centered communities computed for each selected seed. Ego-centered communities are themselves computed applying a recently proposed ensemble ranking based approach that allow to efficiently combine various local modularities used to guide a greedy optimization process. Results of first experiments on real world networks for which a ground truth decomposition into communities are known, argue for the validity of our approach.