Rencheng Sun

Detecting Community Structure in Networks by Propagating Labels of Nodes

Community structure is a common property of many networks. Automatically finding communities in networks can provide invaluable help in understanding the structure and the functionality of networks. Many algorithms to find communities have been developed in recent years. Here we devise a new algorithm to detect communities in networks--propagation algorithm. By propagating the labels of nodes in networks, detecting communities are transformed into analyzing the labels of nodes in networks in this algorithm. Real-world and computer-generated networks are used to verify this algorithm. The results of our experiments indicate that it is sensitive to community structure and effective at discovering communities in networks.

A Sequential Pattern Mining Algorithm Based on Improved FP-tree

Sequential pattern mining is an important data mining problem with broad application. Most of the previously developed sequential pattern mining methods need to scan the database many times. In this study, STMFP algorithm based on improved FP-tree is presented for sequential pattern mining. By improving the FP-tree structure, every node of the tree can store a set of items instead of one item. After scanning the sequential database once time, the tree can store all the sequences. In addition, a novel mining method, combining nodes from leaf to root which helps mining sequential patterns, is proposed. The cost of mining pattern sequence is divided into two parts. One is to construct STMFP Tree. The cost of this part associates with the size of sequential database. Another one is to find random assembled nodes from leaf to root in every path of STMFP tree. Because the maximal length of path is bounded by the maximal length of one transaction, and there are exiting common nodes which help reduce the number of leaf nodes, so the cost of this part must be much less than the size of the database. Compared with other methods which need to scan the sequential database many times, the cost of our method must be less than two passes of the database. Through the whole mining process, it only needs scan the database once time.

Complex network model of the Treatise on Cold Damage Disorders

Abstract Investigating the underlying principles of the Treatise on Cold Damage Disorder is meaningful and interesting. In this study, we investigated the symptoms, herbal formulae, herbal drugs, and their relationships in this treatise based on a multi-subnet composited complex network model (MCCN). Syndrome subnets were constructed for the symptoms and a formula subnet for herbal drugs. By subnet compounding using MCCN, a composited network was obtained that described the treatment relationships between syndromes and formulae. The results obtained by topological analysis suggested some prescription laws that could be validated in clinics. After subnet reduction using the MCCN, six channel (Tai-yang, Yang-ming, Shao-yang, Tai-yin, Shao-yin, and Jue-yin) subnets were obtained. By analyzing the strengths of the relationships among these six channel subnets, we found that the Tai-yang channel and Yang-ming channel were related most strongly with each other, and we found symptoms that implied pathogen movements and transformations among the six channels. This study could help therapists to obtain a deeper understanding of this ancient treatise.

Network Propagation with Dual Flow for Gene Prioritization

Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.

The MagicARPhone: An AR-Based Game on Phone Offering Personalized Enjoyment

Mobile phone is an ideal platform for Augmented Reality. However, there is no related application supplying users with changeable and optional experience of three-dimension models. MagicARPhone, a new type of Augmented Reality game allowing users to see their favorite three-dimension models coexisting with the real world on their smart phone, is proposed in this paper. The phone undertakes the whole AR processing. Various 3-D models are available on MagicARPhone wap site, which allows users to download their fond ones by wireless network. Besides, users could upload their models to share their enjoyment. A Model Parser on the server is created to export the useful model data for reconstructing the visual scene on smart phone.

Boarding Stations Inferring Based on Bus GPS and IC Data

Understanding bus passengers’ flow patterns is an important guide for optimization of public transport system. In most cities buses have been equipped with GPS devices and Smart IC card fare devices, which leads to amount of real-time geocoded data of buses and passengers. Usually these two kinds of data could be combined for inferring passengers’ boarding stations. However, in some cases there is no straightforward relation between them, such that boarding station of passengers are unknown. In this paper, an inferring method for finding the correspondence between these two types of data is proposed. This method was validated by Qingdao data. Based on that, we analyze the spatio-temporal distribution of passengers’ flow.

Weighted Bipartite Network Projection for Personalized Recommendations

Abstract—To reduce the difficulty of personalized recommendations, the traditional network-based method constructed bipartite networks with stronger links (higher ratings). However, weaker links and link weights were almost ignored. Although the existing method effectively mined users’ preferences, it was impossible to catch users’ disgusts. Therefore, this paper proposed a novel method to effectively discover users’ preferences and disgusts. Experimental results on the MovieLens dataset demonstrated that the proposed method was much more superior to the baseline method under the diversity index.

Enteromorpha Prolifera Detection with MODIS Image Using Semi-supervised Clustering

In recent years, enteromorpha prolifera detectionhas received increasing attention. Supervised learning withremote sensing images can achieve satisfactory performancesfor green tide monitoring. However, data distributions betweenimages obviously differ, and it would be too costlyto label a massive amount of images for enteromorphaprolifera detection. Thus, this paper focuses on detectingenteromorpha prolifera using not only limited labelled data,but also a large amount of unlabelled data. We propose aneffective semi-supervised clustering framework for enteromorphaprolifera detection, which can reduce the labellingcost and alleviate the overfitting problem. Experimentalresults prove the effectiveness and potential of our approach,with almost a 15% increase from baseline. In addition, theproposed approach can provide quantitative assessments forband data of moderate resolution imaging spectroradiometer(MODIS) images, and several often ignored bands, such asbands 5, 6, and 7, are shown to be useful for enteromorphaprolifera detection.

The Research of Multi-information Dissemination Models and Simulation

Many phenomena can be regarded as multi-information dissemination existing simultaneously in nature and society, and interaction exists in the course of different information dissemination. Taking complex network as system model complex network dynamics theories are adopted. Multi-information dissemination models are researched and explored in the same system in which multi-information dissemination exists simultaneously. For the process of multi-information dissemination simultaneously existing, there are some problems about dissemination states, such as diversity and complexity. Expression methods and state transition models are given in the interaction of different multi-information dissemination. And the formal description of node states is also given. The rules and regulations of multi-information dissemination, interaction and infection are defined. In the end, three kinds of information dissemination existing simultaneously in a BA complex network as an example, model simulation and analysis are given.