Propagation-based Algorithms Research Articles

A fast community detection algorithm using a local and multi-level label diffusion method in social networks

One of the popular categories of community detection methods are label propagation-based algorithms. Label propagation-based algorithms use local criteria and have a near-linear time complexity. However, these algorithms have problems such as low accuracy, instability, and high computational time in comparison with other local methods. This article presents a fast and simple label diffusion method (FSLD), using local criteria to discover communities accurately in large-scale networks. In FSLD method, community formation is initially started from a low-degree periphery node and then it diffuses its label from outer to inner side of community in a multi-level way. In next step, using a label updating step, all nodes from high-degree to low-degree have the potential to update and finalize their label to obtain initial communities. The experimental results reveal the higher accuracy and performance of the proposed FSLD algorithm in comparison to other state-of-the-art algorithms.

Image Matting: A Comprehensive Survey on Techniques, Comparative Analysis, Applications and Future Scope

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.

Efficient Community Detection Algorithm with Label Propagation using Node Importance and Link Weight

Community detection is a principle tool for analysing and studying of a network structure. Label Propagation Algorithm (LPA) is a simple and fast community detection algorithm which is not accurate enough because of its randomness. However, some advanced versions of LPA have been presented in recent years, but their accuracy need to be improved. In this paper, an improved version of label propagation algorithm for community detection called WILPAS is presented. The proposed algorithm for community detection considers both nodes and links important. WILPAS is a parameter-free algorithm and so requires no prior knowledge. Experiments and benchmarks demonstrate that WILPAS is a pretty fast algorithm and outperforms other representative methods in community detection on both synthetic and real-world networks. More specifically, experiments show that the proposed method can detect the true community structure of real-world networks with higher accuracy than other representative label propagation-based algorithms. Finally, experimental results on the networks with millions of links reveal that the proposed algorithm preserve nearly linear time complexity of traditional LPA. Therefore, the proposed algorithm can efficiently detect communities of large-scale social networks.

A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks

One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.

Minimal-delay distance transform for neighborhood-sequence distances in 2D and 3D

This paper presents a path-based distance, where local displacement costs vary both according to the displacement vector and with the travelled distance. The corresponding distance transform algorithm is similar in its form to classical propagation-based algorithms, but the more variable distance increments are either stored in look-up-tables or computed on-the-fly. These distances and distance transform extend neighborhood-sequence distances, chamfer distances and generalized distances based on Minkowski sums. We introduce algorithms to compute a translated version of a neighborhood sequence distance map both for periodic and aperiodic sequences and a method to derive the centered distance map. A decomposition of the grid neighbors, in Z2 and Z3, allows to significantly decrease the number of displacement vectors needed for the distance transform. Overall, the distance transform can be computed with minimal delay, without the need to wait for the whole input image before beginning to provide the result image.

Target detection through image processing and resilient propagation algorithms

This paper deals with target detection studies using the image processing method as well as resilient propagation-based neural network paradigm. In the resilient propagation-based algorithms, the pre-processing operation to extract features of relevance is done using the moment invariance method. These features are then fed as input to the resilient propagation neural network. RPROP (resilient propagation) is an adaptive technique based on the standard backpropagation algorithm. This RPROP algorithm is also implemented in ADSP-21062 assembly language, since a digital signal processor (DSP) execution is much faster than the normal PC execution, as speed is desirable in real time . It is observed that the resilient propagation-based target detection is better compared to the image processing method of target detection. The main objectives of the paper are the demonstration of the applicability of moment invariant features to neural network-based target detection method and implementation of the technique using a DSP chip, ADSP-21062.