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Search Results: 1 - 10 of 52680 matches for " CHENG Xue-qi "
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Prediction of "Forwarding Whom" Behavior in Information Diffusion
Peng Bao,Hua-Wei Shen,Xue-Qi Cheng
Computer Science , 2014,
Abstract: Follow-ship network among users underlies the diffusion dynamics of messages on online social networks. Generally, the structure of underlying social network determines the visibility of messages and the diffusion process. In this paper, we study forwarding behavior of individuals, taking Sina Weibo as an example. We investigate multiple exposures in information diffusion and the "forwarding whom" problem associated with multiple exposures. Finally, we model and predict the "forwarding whom" behavior of individuals, combining structural, temporal, historical, and content features. Experimental results demonstrate that our method achieves a high accuracy 91.3%.
Spectral methods for the detection of network community structure: a comparative analysis
Hua-Wei Shen,Xue-Qi Cheng
Computer Science , 2010, DOI: 10.1088/1742-5468/2010/10/P10020
Abstract: Spectral analysis has been successfully applied at the detection of community structure of networks, respectively being based on the adjacency matrix, the standard Laplacian matrix, the normalized Laplacian matrix, the modularity matrix, the correlation matrix and several other variants of these matrices. However, the comparison between these spectral methods is less reported. More importantly, it is still unclear which matrix is more appropriate for the detection of community structure. This paper answers the question through evaluating the effectiveness of these five matrices against the benchmark networks with heterogeneous distributions of node degree and community size. Test results demonstrate that the normalized Laplacian matrix and the correlation matrix significantly outperform the other three matrices at identifying the community structure of networks. This indicates that it is crucial to take into account the heterogeneous distribution of node degree when using spectral analysis for the detection of community structure. In addition, to our surprise, the modularity matrix exhibits very similar performance to the adjacency matrix, which indicates that the modularity matrix does not gain desired benefits from using the configuration model as reference network with the consideration of the node degree heterogeneity.
Uncovering the community structure associated with the diffusion dynamics of networks
Xue-Qi Cheng,Hua-Wei Shen
Statistics , 2009, DOI: 10.1088/1742-5468/2010/04/P04024
Abstract: As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of network, which measures how easily the diffusion occurs among different communities. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods at identifying the community structure of networks. Applications on real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the obtained community structure can naturally reflect the diffusion capability of the underlying network.
IMRank: Influence Maximization via Finding Self-Consistent Ranking
Suqi Cheng,Hua-Wei Shen,Junming Huang,Wei Chen,Xue-Qi Cheng
Computer Science , 2014,
Abstract: Influence maximization, fundamental for word-of-mouth marketing and viral marketing, aims to find a set of seed nodes maximizing influence spread on social network. Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy. We first point out that greedy algorithms are essentially finding a self-consistent ranking, where nodes' ranks are consistent with their ranking-based marginal influence spread. This insight motivates us to develop an iterative ranking framework, i.e., IMRank, to efficiently solve influence maximization problem under independent cascade model. Starting from an initial ranking, e.g., one obtained from efficient heuristic algorithm, IMRank finds a self-consistent ranking by reordering nodes iteratively in terms of their ranking-based marginal influence spread computed according to current ranking. We also prove that IMRank definitely converges to a self-consistent ranking starting from any initial ranking. Furthermore, within this framework, a last-to-first allocating strategy and a generalization of this strategy are proposed to improve the efficiency of estimating ranking-based marginal influence spread for a given ranking. In this way, IMRank achieves both remarkable efficiency and high accuracy by leveraging simultaneously the benefits of greedy algorithms and heuristic algorithms. As demonstrated by extensive experiments on large scale real-world social networks, IMRank always achieves high accuracy comparable to greedy algorithms, with computational cost reduced dramatically, even about $10-100$ times faster than other scalable heuristics.
Degree-Strength Correlation Reveals Anomalous Trading Behavior
Xiao-Qian Sun, Hua-Wei Shen, Xue-Qi Cheng, Zhao-Yang Wang
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0045598
Abstract: Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock markets. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying the anomalous traders using the transaction data of eight manipulated stocks and forty-four non-manipulated stocks during a one-year period. By analyzing the trading networks of stocks, we find that the trading networks of manipulated stocks exhibit significantly higher degree-strength correlation than the trading networks of non-manipulated stocks and the randomized trading networks. We further propose a method to detect anomalous traders of manipulated stocks based on statistical significance analysis of degree-strength correlation. Experimental results demonstrate that our method is effective at distinguishing the manipulated stocks from non-manipulated ones. Our method outperforms the traditional weight-threshold method at identifying the anomalous traders in manipulated stocks. More importantly, our method is difficult to be fooled by colluded traders.
Grouped Threshold Digital Signed System
一种分组结构的门限容错签名系统*

YANG Fan,SHA Ying,CHENG Xue-qi,
杨帆
,沙瀛,程学旗

计算机应用研究 , 2007,
Abstract: Previous threshold digital signed system usually stop service when system use proactive secret share scheme,because the whole system has to join the scheme.A Grouped Threshold Digital Signed System(GDSS) was mentioned.The system use proactive secret share scheme and sub-sign verify scheme,these schemes need only part of system to join.So GDSS has high availability and security.
P2P distributed digital signed system
一个P2P分布式数字签名系统

YANG Fan,SHA Ying,CHENG Xue-qi,
杨帆
,沙瀛,程学旗

计算机应用 , 2007,
Abstract: A distributed digital signed system based on P2P Distributed Digital Signed System (PDDSS) was proposed in this paper. This system uses peer node to compute digital signature, which replaces the CA central server. The system has many advantages: low cost, high adaptation, high availability, and intrusion-tolerance. And Verified Secret Share and Proactive Secret Share scheme are used to protect the secret key of CA.
Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network
Peng Bao, Hua-Wei Shen, Wei Chen, Xue-Qi Cheng
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0076027
Abstract: Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.
Covariance, correlation matrix and the multi-scale community structure of networks
Hua-Wei Shen,Xue-Qi Cheng,Bin-Xing Fang
Physics , 2010, DOI: 10.1103/PhysRevE.82.016114
Abstract: Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this article, we consider detecting the multi-scale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multi-scale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multi-scale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multi-scale community structure of network, as well as the translation and rotation transformations.
Triangular clustering in document networks
Xue-qi Cheng,Fu-xin Ren,Shi Zhou,Mao-Bin Hu
Physics , 2008, DOI: 10.1088/1367-2630/11/3/033019
Abstract: Document networks are characteristic in that a document node, e.g. a webpage or an article, carries meaningful content. Properties of document networks are not only affected by topological connectivity between nodes, but also strongly influenced by the semantic relation between content of the nodes. We observe that document networks have a large number of triangles and a high value of clustering coefficient. And there is a strong correlation between the probability of formation of a triangle and the content similarity among the three nodes involved. We propose the degree-similarity product (DSP) model which well reproduces these properties. The model achieves this by using a preferential attachment mechanism which favours the linkage between nodes that are both popular and similar. This work is a step forward towards a better understanding of the structure and evolution of document networks.
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