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Community structure is an important mesoscale topological characteristic of complex networks, which is significant for understanding structural features and organizational functions in networks. Local expansion methods have been proved to be efficient and effective for community detection. However, it has been shown that there are inherent drawbacks for these methods to uncover overlapping communities...
The conventional algorithm (COPRA -- Community Overlap PRopagation Algorithm) proposed by Steve Gregory is efficient and useful in Complex Networks, but it is a challenge to select a suitable parameter "thr" as the input of the algorithm. In this paper, we put forward a threshold based label propagation algorithm, in which each vertex in the network is identified with a threshold respectively,...
Abnormal crowd behavior detection is an important research issue in video processing and computer vision. In this paper we introduce a novel method to detect abnormal crowd behaviors in video surveillance based on interest points. A complex network-based algorithm is used to detect interest points and extract the global texture features in scenarios. The performance of the proposed method is evaluated...
This paper presents a new approach to detect interest points in digital images based on complex network theory. We propose a computable method according to the properties of complex network to each image. We associate a weighted network, analyze the degrees and communities of nodes and can provide visual display of the importance of different interest points in an image. The results show the approach...
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