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Social Network Analysis of Twitter to Identify Issuer of Topic using PageRank

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第1期10卷 107-111页

作者： Priyanta, Sigit Trisna, I. Nyoman Prayana Univ Gadjah Mada Dept Comp Sci & Elect Yogyakarta Indonesia

Twitter as widest micro-blogging and social media proves a billion of tweets from many users. Each tweet carry its own topic, and the tweet itself is can be retweeted by other user. Social network analysis is needed to reach the original issuer of a topic. Representing topic-specific Twitter network can be done to get the main issuer of the topic with graph based ranking algorithm. One of the algorithm is PageRank, which rank each node based on number of in-degree of that node, and inversely proportional to out-degree of the other nodes that point to that node. In proposed methodology, network graph is built from Twitter where user acts as node and tweet-retweet relation as directed edge. User who retweet the tweet points to original user who tweet. From the formed graph, each node's PageRank is calculated as well as other node properties like centrality, degree, and followers and average time retweeted. The result shows that PageRank score of node is directly proportional to closeness centrality and in-degree of node. However, the ranking with PageRank, closeness centrality, and in-degree ranking yield different ranking result.

关键词： Twitter ranking social network analysis graph-based algorithm PageRank graph centrality

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Search Terms Construction for Financial Advertisements Acquisition in Search Engines

Search Terms Construction for Financial Advertisements Acqui...

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作者： Qun Dong Changyong Guo Zhaoxin Zhang Ning Li School of Computer Science and Technology Harbin Institute of Technology

Internet financial advertisements have become the top priority in the digital economy and financial advertisements acquisition is the data basis for all relevant researches. We will study on the construction of search terms, a primary task of obtaining financial advertisements in search engines, which is a problem related to semantic extension. In this paper, an unsupervised, graph-based algorithm that simultaneously incorporates the position of words and their frequency in documents to choose search terms for financial advertisements acquisition is proposed. Experiments are carried out to prove that the algorithm compared with other algorithms is more efficient in terms of the time performance and can find more financial advertisements in search engines.

关键词： nature language processing graph-based algorithm search terms construction

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Towards fast and lightweight spam account detection in mobile social networks through fog computing

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2018年第4期11卷 778-792页

作者： Zhang, Jiahao Li, Qiang Wang, Xiaoqi Feng, Bo Guo, Dong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Minist Educ Changchun 130012 Jilin Peoples R China

Now, mobile devices play an increasingly important role in social networks by sharing information quickly, such as mobile phones and wearable health surveillance devices. Mobile social networks are vulnerable to spammers because of the fragile security policies of mobile operating systems. Especially, social networks on mobile devices face many difficulties in defending against spammers due to their low computing power, poor network quality and long response time. Since graph-based algorithms require huge computing power, machine-learning classifiers require very short response time, and existing PC-based research is not suitable for mobile devices, we need a lightweight and fast response method for mobile devices to detect spammers in mobile social networks. Regarded as the extension of cloud computing, fog computing puts the data, data processing and applications in the devices that are at the edge of the Internet (without storing all of them in the cloud), which leads to a better real-time performance, adapts to the wide geographical distribution and the high mobility of mobile devices. In this paper, we propose COLOR +, a method based on fog computing that performs most computations at terminal (mobile devices). It only uses the interaction between the account and its neighbors, which makes it easy to store and calculate a local graph on a mobile device. Each interaction value can be applied to any request. COLOR + detects spammers based on a threshold of the suspicion degree. We collect 50 million normal accounts and about 40,000 spammers from Twitter. Experiments show that the accuracy of COLOR + is about 85.95%, whose average time to detect an account is 0.01s. Therefore, COLOR + is an effective detection method that can be quickly applied.

关键词： Spammer Interactive relation graph-based algorithm Fog computing

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Contribution to Improve Database Classification algorithms for Multi-Database Mining

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2018年第3期14卷 709-726页

作者： Miloudi, Salim Rahal, Sid Ahmed Khiat, Salim Univ Sci & Technol Mohamed Boudiaf USTOMB Oran Fac Comp Sci & Math Dept Comp Sci Bir El Djir Algeria

Database classification is an important preprocessing step for the multi- database mining (MDM). In fact, when a multi- branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to (n(2)-n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

关键词： Connected Components Database Classification graph-based algorithm Multi-Database Mining

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GroupFound: An effective approach to detect suspicious accounts in online social networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2017年第7期13卷 1-1页

作者： Feng, Bo Li, Qiang Pan, Xiaowen Zhang, Jiahao Guo, Dong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Jilin Peoples R China

Online social networks are an important part of people's life and also become the platform where spammers use suspicious accounts to spread malicious URLs. In order to detect suspicious accounts in online social networks, researchers make a lot of efforts. Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. Besides, such methods are unable to cope with the variable and unknown features. The works based on graph mainly use the location and social relationship of spammers, and they need to build a huge social graph, which leads to much computing cost. Thus, it is necessary to propose a lightweight algorithm which is hard to be evaded. In this article, we propose a lightweight algorithm GroupFound, which focuses on the structure of the local graph. As the bi-followers come from different social communities, we divide all accounts into different groups and compute the average number of accounts for these groups. We evaluate GroupFound on Sina Weibo dataset and find an appropriate threshold to identify suspicious accounts. Experimental results have demonstrated that our algorithm can accomplish a high detection rate of 86.27% at a low false positive rate of 8.54%.

关键词： Online social networks community suspicious account graph-based algorithm threshold

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Sense Disambiguation For Marathi Language Words Using Decision graph Method

Sense Disambiguation For Marathi Language Words Using Decisi...

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World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave)

作者： Zungre, Nutan B. Dhopavkar, Gauri M. Yashwantrao Chavan Coll Engn Dept Comp Sci & Engn Hingna Rd Nagpur 41110 Maharashtra India

ISBN: (纸本)9781467392143

"Sense Disambiguation" is a simple task of assigning appropriate sense (meaning) for an ambiguous word in a given context. Word sense disambiguation is very crucial, and it is used in many application of computational linguistics. In Natural Language Processing, many methods are available for performing the word sense disambiguation. In our proposed work, we are using graph-based algorithm, through which word ambiguity is resolved based on their senses and context domain. In graph-based algorithm, a graph is created that comprises the word which is to be disambiguated with their corresponding candidate sense. In the proposed work, sense disambiguation has been done for Marathi language words. Through Marathi WordNet [4] prepared by IIT-Bombay, multiple senses of Marathi word has been explored.

关键词： Word Sense Disambiguation Natural Language Processing graph-based algorithm Ambiguity Marathi WordNet

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GHAR: graph-based Hybrid Adaptive Routing for Cognitive Radio based Disaster Response Networks

GHAR: Graph-based Hybrid Adaptive Routing for Cognitive Radi...

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IEEE International Conference on Communications (ICC)

作者： Ngo, Thuan Nishiyama, Hiroki Kato, Nei Kotabe, Satoshi Tohjo, Hiroshi Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan NTT Corp NTT Network Innovat Labs Yokosuka Kanagawa Japan

ISBN: (纸本)9781479966646

Although the importance of Disaster Response Networks (DRNs) has been highlighted in many researches, the requirement of spectrum agility has not been well addressed. In this paper, we focus on using all-spectrum cognitive radio for DRNs to fulfill this requirement. We consider a DRN constructed by Cognitive Radio Base Stations (CRBSs), which are deployed in the disaster affected area. Each CRBS is equipped with multiple antennas to support different frequency bands available in the area. based on the considered DRN, we propose a graph-based Hybrid Adaptive Routing scheme, which we refer to as GHAR. There are two phases in GHAR, centralized phase for topology formation and distributed phase for adaptive routing. In the centralized phase, we propose an algorithm that unites k non-overlapping minimum spanning trees to construct the topology for the next phase. We provide an analysis on the relationship between k and the adaptability with cognitive radio as well as the complexity of routing process. We also provide an analysis on the optimality of k. Furthermore, extensive simulations are conducted to validate our analysis. Simulation results confirm the effectiveness of our proposal and the existence of the optimal value of k.

关键词： Cognitive radio adaptive routing graph-based algorithm disaster response network

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A graph-based method for blood vessel segmentation of retinal images 9th

A graph-based method for blood vessel segmentation of retina...

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9th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Zhang, J. D. Jiang, W. H. Zhang, C. X. Cui, Y. J. Shenzhen Inst Informat Technol Dept Elect & Commun Shenzhen Peoples R China

ISBN: (纸本)9781315683010;9781138027848

Segmentation of blood vessels from retinal images plays a crucial role for diagnosing eye diseases. In this paper, we propose an automatic segmentation method for blood vessels in retinal images. Firstly, we preprocess the retinal images to enhance vessels and overcome the lighting variation. Then, a graph-based algorithm is exploited to partition the retinal image into multiple regions. Finally, we classify each region as retinal region or non-vessel region with K-mean algorithm. The proposed method is validated on the publicly available DRIVE database, and compared with the state-of-the-art algorithms.

关键词： medical image segmentation retinal images graph-based algorithm K-mean algorithm

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EMR: A Scalable graph-based Ranking Model for Content-based Image Retrieval

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第1期27卷 102-114页

作者： Xu, Bin Bu, Jiajun Chen, Chun Wang, Can Cai, Deng He, Xiaofei Zhejiang Univ Zhejiang Prov Key Lab Serv Robot Coll Comp Sci Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab CAD & CG Coll Comp Sci Hangzhou 310027 Peoples R China

graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model - the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications.

关键词： graph-based algorithm ranking model image retrieval out-of-sample

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Semantic-based Web API Composition for Data Mashups

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2015年第4期31卷 1233-1248页

作者： Lee, Yong-Ju Kyungpook Natl Univ Sch Comp Sci & Engn Taegu 702701 South Korea

With the growing popularity of data mashups, the number of Web APIs has increased significantly. As a result, finding and composing the right APIs has become an increasingly complex task. Although several tools such as Yahoo's Pipes, IBM's Lotus Mashup, and Intel's Mashmaker have been developed to enable users to create data mashups without programming skills, there are several challenging issues when combining a large number of APIs into the data mashup. This paper proposes novel algorithms for the automatic discovery and composition of Web APIs. Our discovery algorithm adopts strategies that rapidly prune APIs that are guaranteed not to match the query. Our composition algorithm consists of constructing a composable similarity graph (CSG) and searching composition candidates. The CSG presents the semantic functional dependency between the inputs and the outputs of the Web APIs. Using this graph, we generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. We evaluate the algorithms on a real-world dataset from ***, and show that they can produce the results satisfying the user's desired output.

关键词： data mashup Web API discovery composition ontology learning graph-based algorithm

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