Abstract Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article, we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.
@cite_11 extended LabelRank @cite_1 algorithm which is a stabilized and deterministic variant of Label propagation algorithm @cite_16 to deal with evolving communities in dynamic networks. The extended algorithm called LabelRankT is based on a conditional update rule by which only nodes involved in change between two consecutive snapshots are updated.
@cite_16 @cite_1 @cite_11
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An increasingly important challenge in network analysis is efficient detection and tracking of communities in dynamic networks for which changes arrive as a stream. There is a need for algorithms that can incrementally update and monitor communities whose evolution generates huge realtime data streams, such as the Internet or on-line social networks. In this paper, we propose LabelRankT, an online distributed algorithm for detection of communities in large-scale dynamic networks through stabilized label propagation. Results of tests on real-world networks demonstrate that LabelRankT has much lower computational costs than other algorithms. It also improves the quality of the detected communities compared to dynamic detection methods and matches the quality achieved by static detection approaches. Unlike most of other algorithms which apply only to binary networks, LabelRankT works on weighted and directed networks, which provides a flexible and promising solution for real-world applications. An increasingly important challenge in network analysis is efficient detection and tracking of communities in dynamic networks for which changes arrive as a stream. There is a need for algorithms that can incrementally update and monitor communities whose evolution generates huge real-time data streams, such as the Internet or on-line social networks. In this paper, we propose LabelRankT, an on-line distributed algorithm for detection of communities in large-scale dynamic networks through stabilized label propagation. Results of tests on real-world networks demonstrate that LabelRankT has much lower computational costs than other algorithms. It also improves the quality of the detected communities compared to dynamic detection methods and matches the quality achieved by static detection approaches. Unlike most of other algorithms which apply only to binary networks, LabelRankT works on weighted and directed networks, which provides a flexible and promising solution for real-world applications. An important challenge in big data analysis nowadays is detection of cohesive groups in large-scale networks, including social networks, genetic networks, communication networks and so. In this paper, we propose LabelRank, an efficient algorithm detecting communities through label propagation. A set of operators is introduced to control and stabilize the propagation dynamics. These operations resolve the randomness issue in traditional label propagation algorithms (LPA), stabilizing the discovered communities in all runs of the same network. Tests on real-world networks demonstrate that LabelRank significantly improves the quality of detected communities compared to LPA, as well as other popular algorithms. We study a dynamic labeling problem for points on a line that is closely related to labeling of zoomable maps. Typically, labels have a constant size on screen, which means that, as the scale of the map decreases during zooming, the labels grow relatively to the set of points, and conicts may occur due to overlapping labels. Our algorithmic problem is a combined dynamic selection and placement problem in a sliding-label model: (i) select for each label ‘ a contiguous active range of map scales at which ‘ is displayed, and (ii) place each label at an appropriate position relative to its anchor point by sliding it along the point. The active range optimization (ARO) problem is to select active ranges and slider positions so that no two labels intersect at any scale and the sum of the lengths of active ranges is maximized. We present a dynamic programming algorithm to solve the discrete k-position ARO problem optimally and an FPTAS for the continuous sliding ARO problem.