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Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network

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JOURNAL OF THEORETICAL BIOLOGY 2017年 430卷 9-20页

作者： Wen, Yu-Ting Lei, Hai-Jun You, Zhu-Hong Lei, Bai-Ying Chen, Xing Li, Li-Ping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518060 Guangdong Peoples R China Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen 518060 Guangdong Peoples R China Guangdong Key Lab Biomed Measurements & Ultrasoun Shenzhen 518060 Guangdong Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 21116 Jiangsu Peoples R China

Prediction of protein-protein interactions (PPIs) is of great significance. To achieve this, we propose a novel computational method for PPIs prediction based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins. Specifically, the physical and chemical properties of protein are the protein amino acid mutation rate and its hydrophobicity, respectively. The amino acid mutation rate is extracted using a BLOSUM62 matrix, which puts the protein sequence into block substitution matrix. The SNF model is exploited to fuse protein physical and chemical features of multiple data by iteratively updating each original network. Finally, the complementary features from the fused network are fed into a label propagation algorithm (LPA) for PPIs prediction. The experimental results show that the proposed method achieves promising performance and outperforms the traditional methods for the public dataset of H. pylori, Human, and Yeast. In addition, our proposed method achieves average accuracy of 76.65%, 81.98%, 84.56%, 84.01% and 84.38% on E. coli, C. elegans, H. sapien, H. pylori and M. musculus datasets, respectively. Comparison results demonstrate that the proposed method is very promising and provides a cost-effective alternative for predicting PPIs. The source code and all datasets are available at http://*** dF7rp7N. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Protein-protein interactions Prediction Similarity network fusion label propagation algorithm

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Community Discovering algorithm based on Nodes with Maximum Degree and label propagation

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 115-126页

作者： Li Shengli Chen Deyun Yao Yuanzhe Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

label propagation algorithm (LPA algorithm) is gotten widely attention by its simplicity, rapidity and greater effectiveness. Aimed at the problem of label updating process is sensitive to the order of nodes in LPA algorithm, an improved algorithm is proposed in this paper. The algorithm starts from the nodes with maximum degree in the network, according to its neighbor nodes' community similarity index to decide whether its label setting, and then completes label setting in the first round;On this basis, it continues to carry out the iteration in label propagation algorithm, completes the entire network of community structure detection. The experimental results show that the improved algorithm is slightly better than the original algorithm at running time and number of iterations, has higher robustness, prevents the occurrence of the trivial solution.

关键词： Community Detection label propagation algorithm Nodes with Maximum degree Neighbour Node

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AnnoFin-A hybrid algorithm to annotate financial text

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 88卷 270-275页

作者： Das, Ananda Swarup Mehta, Sameep Subramaniam, L. V. IBM India Res Labs New Delhi India

In this work, we study the problem of annotating a large volume of Financial text by learning from a small set of human-annotated training data. The training data is prepared by randomly selecting some text sentences from the large corpus of financial text. Conventionally, bootstrapping algorithm is used to annotate large volume of unlabeled data by learning from a small set of annotated data. However, the small set of annotated data have to be carefully chosen as seed data. Thus, our approach is a digress from the conventional approach of bootstrapping as we let the users randomly select the seed data. We show that our proposed algorithm has an accuracy of 73.56% in classifying the financial texts into the different categories ("Accounting", "Cost", "Employee", "Financing", "Sales", "Investments", "Operations", "Profit", "Regulations" and "Irrelevant") even when the training data is just 30% of the total data set. Additionally, the accuracy improves by an approximate average of 2% for an increase of the training data by 10% and the accuracy of our system is 77.91% when the training data is about 50% of the total data set. As a dictionary of hand chosen keywords prepared by domain experts are often used for financial text extraction, we assumed the existence of almost linearly separable hyperplanes between the different classes and therefore, we have used Linear Support Vector Machine along with a modified version of label propagation algorithm which exploits the notion of neighborhood (in Euclidean space) for classification. We believe that our proposed techniques will be of help to Early Warning Systems used in banks where large volumes of unstructured texts need to be processed for better insights about a company. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Text classification Financial sentences label propagation algorithm SVM Clustering

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Research Notes: User Influence in Microblog Based on Interest Graph

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International Journal of Software Engineering and Knowledge Engineering 2018年第2期28卷 207-217页

作者： Lidong Zhai Jingya Wang Chaojian Hu Jun Li Institute of Computing Technology Chinese Academy of Sciences Haidian District Beijing 100190 P. R. China Tencent Xuanwu Lab Haidian District Beijing 100193 P. R. China

Microblog is currently the largest social networking platform in China. In recent years, as a social media, the influence of microblog continues to expand. The users who have large influence play a guiding role in the spread of microblog, and even guide the trends of public opinion. Therefore, we propose an influence analysis method to find microblog users who are with great influence, which is of great significance for the research and mining of microblog. User influence analysis in microblog has great difficulties due to the limited amount of microblog information, quick updates and nonstandard microblog language. First, we use the label propagation algorithm combined with LDA algorithm to divide users by the user interest graph, according to the social relationship of microblog users and the content they generate. Then, depending on different interest areas, an improved PageRank algorithm based on user interaction behavior is proposed to calculate the user’s influence. Experiments on the real datasets show that the proposed method outperforms the traditional algorithms.

关键词： Microblogs LDA label propagation algorithm interest graph influence calculation

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label propagation Based Community Detection algorithm with Dpark 1

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4th International Conference on Computational Social Network (CSoNet)

作者： Wang, Ting Qian, Xu Wang, Xiaomeng China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China

ISBN: (数字)9783319217864

ISBN: (纸本)9783319217864;9783319217857

Numerous methods for detecting communities on social networks have been proposed in recent years. However, the performance and scalability of the algorithms are not enough to work on the real-world large-scale social networks. In this paper, we propose Improved Speaker-listener label propagation algorithm (iSLPA), an efficient and fully distributed method for community detection. It is implemented with Dpark, which is a Python version of Spark and a lightning-fast cluster computing framework. To the best of our knowledge, this is the first attempt at community detection on Dpark. It can automatically work on three kinds of networks: directed networks, undirected networks, and especially bipartite networks. In iSLPA, we propose a new initialization and updating strategy to improve the quality and scalability for detecting communities. And we conduct our experiments on real-world social networks datasets on both benchmark networks and Douban (http://***) user datasets. Experimental results demonstrate that iSLPA has a comparable performance than SLPA, and have confirmed our algorithms is very efficient and effective on the overlapping community detection of large-scale networks.

关键词： Community detection label propagation algorithm Dpark iSLPA

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Quantitative function and algorithm for community detection in bipartite networks

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INFORMATION SCIENCES 2016年 367卷 874-889页

作者： Li, Zhenping Wang, Rui-Sheng Zhang, Shihua Zhang, Xiang-Sun Beijing Wuzi Univ Sch Informat Beijing Peoples R China Harvard Med Sch Brigham & Womens Hosp Dept Med Boston MA USA Acad Math & Syst Sci CAS Natl Ctr Math & Interdisciplinary Sci Beijing Peoples R China

Community detection in complex networks is a topic of high interest in many scientific fields. A bipartite network is a special type of complex network whose nodes are decomposed into two disjoint sets such that no two nodes within the same set are adjacent. Many relationships in real-world systems can be represented by a bipartite network, such as predator-prey relationships, plant-pollinator interactions, and drug-target interactions. While community detection in unipartite networks has been extensively studied in the past decade, identification of modules or communities in bipartite networks is still in its early stage. Several quantitative functions have been developed for evaluating the quality of bipartite network divisions, however, these functions were designed based on null model comparisons and thus are subject to certain resolution limits. In this paper, we propose a new quantitative function called bipartite partition density for community detection in bipartite networks, and use some network examples to demonstrate that this quantitative function is superior to the widely used Barber's bipartite modularity and other functions. Based on the bipartite partition density, the bipartite network community detection problem is formulated into an integer nonlinear programming model in which a bipartite network can be partitioned into reasonable overlapping communities by maximizing the quantitative function. We further develop a heuristic adapted label propagation algorithm (BiLPA) to optimize the bipartite partition density in large-scale bipartite networks. BiLPA is efficient and does not require any prior knowledge about the number of communities in the networks. We conduct extensive experiments on simulated and real-world networks and demonstrate that BiLPA can successfully identify the community structures of bipartite networks. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Bipartite network Community Quantitative function Integer programming label propagation algorithm

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RESEARCH ON THE algorithm OF COMMUNITY DISCOVERY BASED ON THE STANDARD DEVIATION OF EDGE - BETWEENNESS 13

RESEARCH ON THE ALGORITHM OF COMMUNITY DISCOVERY BASED ON TH...

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Tang, Yanling Li, Jianping Zhao, Gaofeng Wang, Yangyang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781509061266

In this paper, we mainly do the research about the non-overlapping community discovery. We use the GN algorithm and LP algorithm to test the randomly generated network, and compare the two algorithms in two layers, which are the time complexity level, and the static network's synchronization capability level. The simulation results show that the two algorithms are applicable to different scenes.

关键词： Community discovery Girvan-Newman algorithm label propagation algorithm Standard deviation of edge-betweenness

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Detecting Network Community by Propagating labels Based on Contact-Specific Constraint 11th

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11th International Scientific Conference on Future Information Technology (FutureTech) / 10th International Conference on Multimedia and Ubiquitous Engineering (MUE)

作者： Wu, Xiaolan Zhang, Chengzhi Nanjing Univ Sci & Technol Dept Informat Management Nanjing 210094 Jiangsu Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Anhui Peoples R China Key Lab Data Serv & Knowledge Engn Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)9789811015366;9789811015359

As a famous community detection algorithm with near linear time complexity, label propagation algorithm (LPA) has been an active research direction. However, that LPA regards that all the neighbor have the same importance to target node in target node's label update process make it get stuck in poor stability. From the view of structural holes in sociology, people who fill structural holes bring social order to the network, so we improve LPA based on contact-specific constraint measuring structural holes. Experiments show that our improvement successfully detects communities with the highest stability in several commonly used real-world and synthetic networks.

关键词： label propagation algorithm Community detection Network constraint Structural holes Social network

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Optimized Parallel label propagation based Community Detection on the Intel® Xeon Phi™ Architecture 27

Optimized Parallel Label Propagation based Community Detecti...

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IEEE 27th International Symposium on Computer Architecture and High Performance Computing

作者： Khlopotine, Andrei B. Sathanur, Arun V. Jandhyala, Vikram Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781467380119

Complex systems such as social, biological and information networks, characterized by millions to billions of sub-entities and relationships between them are best represented by graphs. A distinguishing feature of such complex systems is the self-organization into dense clusters called communities. Detecting such communities in massive graphs is critical to the understanding of such complex systems. However community detection is non-trivial and is expensive due to a vast number of computations needed to fully realize the underlying relationships. While a number of near-exact and heuristic algorithms is available for community detection, parallelizing such algorithms to fully leverage the advantages of parallel hardware is still a challenging problem. Most presently available approaches attempt to optimize run-time complexities by scaling original serial community detection algorithms. Graph algorithms in general and existing community detection algorithms in particular are known for not being commensurate with linear scalability on parallel systems. Therefore, there is a need for a combination of high-performance software and a hardware platform that would support efficient parallel graph processing with respect to community detection. We present an Intel (R) Xeon Phi (TM) label propagation algorithm (PLPA) variant of community detection algorithm based on label propagation (LP). Our algorithm was tuned for the Intel (R) Xeon Phi (TM) platform a novel architecture that provides 50+ physical cores with simultaneous multithreading. We outline Phi (TM) architecture advantages and limitations for PLPA and massive graph processing in general. We present test results of running our algorithm on large real-world networks while achieving near linear speedups and improving the quality of the detected communities. We also analyze possibilities of processing massive networks that cannot fully fit in a Phi (TM) memory and hence we extend our initial solution to a modified PLPA

关键词： Social Networks label propagation algorithm Parallel Computing Many Integrated Core Architecture

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Detecting community structure using label propagation with consensus weight in complex network

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Chinese Physics B 2014年第9期23卷 594-601页

作者：梁宗文李建平杨帆 Athina Petropulu School of Computer Science and Engineering University of Electronic Science and Technology of China Electrical and Computer Engineering Department RutgersThe State University of New Jersey

Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight （LPAcw）, and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions.

关键词： label propagation algorithm community detection consensus cluster complex network

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