The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one's co-authorship network arranged into a set of "circles", which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks.In this paper, we propose an unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. Our model is an unsupervised method which combines a variety of node features and node similarity measures. The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. In the first level of evaluation, our model achieves 13.33% improvement in terms of overlapping modularity compared to the best among four state-of-the-art community detection methods. Further, we conduct a task-based evaluation -two basic frameworks for collaboration prediction are considered with the circle information (obtained from our model) included in the feature set. Experimental results show that including the circle information detected by our model improves the prediction performance by 9.87% and 15.25% on average in terms of AU C (Area under the ROC) and P rec@20 (Precision at Top 20) respectively compared to the case, where the circle information is not present.
Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.A fundamental problem in understanding the behavior of complex networks is the ability to correctly detect communities. Communities are groups of entities (represented as vertices) that are more connected to each other as opposed to other entities in the system. Mathematically, this question can be translated to a combinatorial optimization problem with the goal of optimizing a given metric of interrelation, such as modularity or conductance. The goodness of community detection algorithms (see 1,2 for a review) is often objectively measured according to how well they achieve the optimization.However, these algorithms can be applied to any network, regardless of whether it possesses a community structure or not. Furthermore when the optimization problem is NP-hard, as in the case of modularity 3 , the order in which vertices are processed as well as the heuristics can change the results. These inherent fluctuations of the results associated with modularity have long been a source of concern among researchers. Indeed the goodness of modularity as an indicator of community structure has also been questioned, and there exist examples 5 which demonstrate that high modularity does not always indicate the correct community structure. Other orthogonal graph-theoretic metrics (e.g., conductance 6 which is also NP-complete 4 ) have been proposed in the past literatures to judge the goodness of a community detection algorithm.Research in addressing the fluctuations in the results due to modularity maximization heuristics include identifying stability among communities from the consensus networks built from the successive iterations of a non-deterministic community detection algorithm (such as by Seifi et al. 7 ). Lancichinetti et al. 8 proposed consensus clustering by reweighting the edges based on how many times the pair of vertices were allocated to the same community, for different identification methods. Delvenne et al. 9 introduced the notion of the stability of a partition, a me...
Abstract. Dynamic complex networks are used to model the evolving relationships between entities in widely varying fields of research such as epidemiology, ecology, sociology, and economics. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of vertices with dense connections within groups and sparser connections between groups. Detecting the evolution of communities within dynamically changing networks is crucial to understanding complex systems. In this paper, we develop a fast community detection algorithm for real-time dynamic network data. Our method takes advantage of community information from previous time steps and thereby improves efficiency while maintaining the quality of community detection. Our experiments on citation-based networks show that the execution time improves as much as 30% (average 13%) over static methods.
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