Chinese Digital Bibliography & Library Project C-DBLP is a huge and real-life co-author social network in China, rarely cited by published paper. It contains a large amount of ground-truth community structure with distinguished research topics. Despite the fact that rich studies on community detection have been conducted with gains of practically fruitful algorithms, unfortunately, with the coming of 'Big Data' era and speedy development of mobile devices, social networks like C-DBLP have incredibly expanded on nodes and edges, as a result, because of massive data cardinality, a large portion of community detection methods consume memory resource excessively. Therefore, in this work, we select Based on Structural Connection Hierarchical Exploration BSCHE algorithm to partition nodes in C-DBLP because of its On time cost, fast enough to process massive data, and its novel physical meaning of similarity between nodes defined by structural connection and availability. In addition, in order to avoid huge memory resource consumption caused by 'Big Data' of C-DBLP, we strengthen BSCHE as a framework BSCHEF by our proposed 'count-pointer-strategy' imitated from incremental batch process to detect co-author communities on C-DBLP. The experiment results show that BSCHEF can find sets of communities on C-DBLP more effectively with the highest modularity value and the least execution time compared to other clustering algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

Detect structural-connected communities based on BSCHEF in C-DBLP

In the field of Venture Capital (VC), researchers have found that VC companies are more likely to jointly invest with other VC companies. This paper attempts to realize a semi-supervised community detection of the VC network based on the data of VC networking and the list of industry leaders. The main research method is to design the initial label of community detection according to the evolution of components of the VC industry leaders. The results show that the community structure of the VC network has obvious distinguishing characteristics, and the aggregation of these communities is affected by the type of institution, the source of capital, the background of personnel, and the field of investment and the geographical position. Meanwhile, by comparing the results of the semi-supervised community detection algorithm with the results of community detection using extremal optimization, it can be shown to some extent that the semi-supervised community detection results in the VC network are more accurate and reasonable.

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How to Better Identify Venture Capital Network Communities: Exploration of A Semi-Supervised Community Detection Method

Label propagation algorithm (LPA) is one of the classical community detection algorithms, with high efficiency, quick speed and no need for any prior information. However, it has the disadvantage of poor stability, which causes the detection results to be random. In order to improve the stability of label propagation algorithm, an algorithm with an adjustable parameter based on clustering coefficient and label propagation is proposed in this article. The algorithm is divided into two steps. The first step is to prioritize the nodes according to their degree and clustering coefficient, and initialize the label according to the ranking result. In the process of initializing the label, only the nodes with clustering coefficient in a certain range are selected to filter out the noisy nodes. The second step is based on the first step. In order to avoid randomness, the neighbor nodes are sorted according to their clustering coefficient and degree, the optimal neighbor node is selected to update the label. By applying the algorithm to LFR artificial network data sets and real networks data sets, the results show that the algorithm reduces the randomness of the label propagation algorithm, enhances the stability and accuracy of detection result, and its adjustable parameter make it possible to have a good quality of community division for various types of networks.

Research on Label Propagation Algorithms Based on Clustering Coefficient

Weighted Label Propagation Algorithm with Probability Threshold (P-WLPA) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, parallel P-WLPA algorithm for labeling big data is conducted. The algorithm sets prior conditions when configuring undirected graph and optimizes parameter learning in P-WLPA process. Through experiments, we analyze the relationship between Iterations for convergence, sample stability threshold and error rate. Finally Serial and parallel P-WLPA performance comparision demonstrates the feasibility and efficiency of the parallel P-WLPA algorithm implementation on Spark.

http://ieeexplore.ieee.org/iel7/7057182/7064993/07065087.pdf

P-WLPA algorithm research on parallel framework Spark

With the development of mobile Internet, it is changing the way we communicate with others. Internet media have gradually become the main mobile crowdsourcing applications for information dissemination and user communication, including online news and social networks. However, the potential business opportunities have stimulated the emergence of a large number of spammers, who release false speech, advertisements, pornographic contents, and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, the research on the identification technology of spammers has been carried out extensively. However, the traditional technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the label propagation algorithm (LPA), making full use of the user comment behaviors and contents. First of all, we collect a large amount of news and comments from NetEase News and label some users in the data as spammers or normal users manually to construct a labeled dataset. Then, a set of behavioral and semantic features are extracted and quantified from the user comment behaviors and comment contents by statistical analysis. Next, we propose the identification technology based on the LPA. Finally, the set of feature values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine the most effective combination of features and improve the technology. The results show that the technology proposed in this paper involves a lower data cost but a better identification effect than some traditional technologies based on the supervised classifier.

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An Effective Identification Technology for Online News Comment Spammers in Internet Media

This article introduced the theoretical study of label propagation algorithm,analysed its characteristics and summarized its applications in multimedia information processing,retrieval,annotation,classification and community discovery,etc.Finally,this paper proposed the future prospects and the trends of developments of the LPA algorithm.

Overview on label propagation algorithm and applications

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

https://www.mdpi.com/1424-8220/22/20/7944/pdf?version=1666259206

A Novel Adaptive Deskewing Algorithm for Document Images

Density-based reliable and robust explainer for counterfactual explanation

Temporal action detection (TAD) aims to recognize actions as well as their corresponding time spans from an input video. While techniques exist that accurately recognize actions from manually trimmed videos, current TAD solutions often struggle to identify the precise temporal boundaries of each action, which are required in many real applications. This paper addresses this problem with a novel Boundary Trimming Module (BTM), a post-processing method that adjusts the temporal boundaries of the detected actions from existing TAD solutions. Specifically, BTM operates based on the classification of frames in the input video, aiming to detect the action more accurately by adjusting the surrounding frames of the start and end frames of the original detection results. Experimental results on the THUMOS14 benchmark data set demonstrate that the BTM significantly improves the performance of several existing TAD methods. Meanwhile, we establish a new state of the art for temporal action detection through the combination of BTM and the previous best TAD solution.

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Shi Wen

Papers

Overview on label propagation algorithm and applications

A Novel Adaptive Deskewing Algorithm for Document Images

Density-based reliable and robust explainer for counterfactual explanation

BTM: Boundary Trimming Module for Temporal Action Detection

A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt