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A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

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Journal of Central South University 2016年第1期23卷 150-159页

作者： Hamed Dinari Hassan Naderi Web and Search Engines Laboratory School of Computer EngineeringIran University of Science and Technology

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

关键词： graph query processing frequent subgraph graph mining data mining positional inverted index

Discovering Association Rules with graph Patterns in Temporal Networks

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Tsinghua Science and Technology 2023年第2期28卷 344-359页

作者： Chu Huang Qianzhen Zhang Deke Guo Xiang Zhao Xi Wang Science and Technology on Information Systems Engineering Laboratory College of Systems EngineeringNational University of Defense TechnologyChangsha 410073China

Discovering regularities between entities in temporal graphs is vital for many real-world applications(e.g.,social recommendation,emergency event detection,and cyberattack event detection).This paper proposes temporal graph association rules(TGARs)that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and *** introduce quality measures(e.g.,support,confidence,and diversification)to characterize meaningful TGARs that are useful and *** addition,the proposed support metric is an upper bound for alternative metrics,allowing us to guarantee a superset of *** extend conventional confidence measures in terms of maximal occurrences of *** diversification score strikes a balance between interestingness and *** the problem is NP-hard,we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal *** propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as ***,we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search *** experimentally verify the effectiveness,efficiency,and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

关键词： temporal networks graph association rule subgraph pattern matching graph mining big graphs

Mako:A graph-based Pattern Growth Approach to Detect Complex Structural Variants

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 205-218页

作者： Jiadong Lin Xiaofei Yang Walter Kosters Tun Xu Yanyan Jia Songbo Wang Qihui Zhu Mallory Ryan Li Guo Chengsheng Zhang The Human Genome Structural Variation Consortium Charles Lee Scott E.Devine Evan E.Eichler Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Leiden Institute of Advanced Computer Science Faculty of ScienceLeiden UniversityLeiden 2311EZNetherland School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Institute for Genome Sciences University of Maryland School of MedicineBaltimoreMD 21201USA Department of Genome Sciences University of Washington School of MedicineSeattleWA 98119USA Howard Hughes Medical Institute University of WashingtonSeattleWA 98195USA The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China 不详

Complex structural variants(CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural ***,detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match *** a result,there has been limited progress for CSV discovery compared with simple structural ***,we systematically analyzed the multi-breakpoint connection feature of CSVs,and proposed Mako,utilizing a bottom-up guided model-free strategy,to detect CSVs from paired-end short-read ***,we implemented a graph-based pattern growth approach,where the graph depicts potential breakpoint connections,and pattern growth enables CSV detection without pre-defined *** evaluations on both simulated and real datasets revealed that Mako outperformed other ***,validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%,where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp,***,the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types,including two novel types of adjacent segment swap and tandem dispersed *** analysis of these CSVs also revealed the impact of sequence homology on the formation of *** is publicly available at https://***/xjtu-omics/Mako.

关键词： Next-generation sequencing Complex structural variant Pattern growth graph mining Formation mechanism

An efficient parallel algorithm of N-hop neighborhoods on graphs in distributed environment

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Frontiers of Computer Science 2019年第6期13卷 1309-1325页

作者： Wenjie LIU Zhanhuai LI School of Computer Northwestern Polytechnical UniversityXi’an 710072China

N-hop neighborhoods information is very useful in analytic tasks on large-scale graphs,like finding clique in a social network,recommending friends or advertising links according to one’s interests,predicting links among websites and *** get the N-hop neighborhoods information on a large graph,such as a web graph,a twitter social graph,the most straightforward method is to conduct a breadth first search(BFS)on a parallel distributed graph processing framework,such as Pregel and ***,due to the massive volume of message transfer,the BFS method results in high communication cost and has low *** this work,we propose a key/value based method,namely KVB,which perfectly fits into the prevailing parallel graph processing framework and computes N-hop neighborhoods on a large scale graph *** the BFS method,our method need not transfer large amount of neighborhoods information,thus,significantly reduces the overhead on both the communication and intermediate results in the distributed *** formalize the N-hop neighborhoods query processing as an optimization problem based on a set of quantitative cost metrics of parallel graph ***,we propose a solution to efficiently load only the relevant neighborhoods for ***,we prove the optimal partial neighborhoods load problem is NP-hard and carefully design a heuristic *** have implemented our algorithm on a distributed graph frameworkSpark graphX and validated our solution with extensive experiments over a number of real world and synthetic large graphs on a modest indoor *** show that our solution generally gains an order of magnitude speedup comparing to the state-of-art BFS implementation.

关键词： N-hop neighborhoods graph mining parallel computing distributed computing

Efficient keyword search over graph-structured data based on minimal covered r-cliques

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Frontiers of Information Technology & Electronic Engineering 2020年第3期21卷 448-465页

作者： Asieh GHANBARPOUR Khashayar NIKNAFS Hassan NADERI School of Computer Engineering Iran University of Science and TechnologyTehran 13114-16846Iran Department of Computer Engineering University of Sistan and BaluchestanZahedan 98167-45845Iran

Keyword search is an alternative for structured languages in querying graph-structured data.A result to a keyword query is a connected structure covering all or part of the queried *** textual coverage and structural compactness have been known as the two main properties of a relevant result to a keyword *** previous works examined these properties after retrieving all of the candidate results using a ranking function in a comparative ***,this needs a time-consuming search process,which is not appropriate for an interactive system in which the user expects results in the least possible *** problem has been addressed in recent works by confining the shape of results to examine their coverage and compactness during the ***,these methods still suffer from the existence of redundant nodes in the retrieved *** this paper,we introduce the semantic of minimal covered r-clique(MCCr)for the results of a keyword query as an extended model of existing *** propose some efficient algorithms to detect the MCCrs of a given *** algorithms can retrieve a comprehensive set of non-duplicate MCCrs in response to a keyword *** addition,these algorithms can be executed in a distributive manner,which makes them outstanding in the field of keyword *** also propose the approximate versions of these algorithms to retrieve the top-k approximate MCCrs in a polynomial *** is proved that the approximate algorithms can retrieve results in *** experiments on two real-world datasets confirm the efficiency and effectiveness of the proposed algorithms.

关键词： Keyword search graph mining Information retrieval Database Clique

Distributed Truss Computation in Dynamic graphs

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Tsinghua Science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

Human Verification over Activity Analysis via Deep Data mining

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Computers, Materials & Continua 2023年第4期75卷 1391-1409页

作者： Kumar Abhishek Sheikh Badar ud din Tahir Senior Member IEEESeattle98103USA Department of Software Engineering Capital University of Science and Technology(CUST)Islamabad44000Pakistan

Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and *** human interaction using RGB images is one of the most complex machine learning tasks in recent *** models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB *** paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based *** use human interaction image sequences as input data,we first perform a few denoising ***,human-to-human analyses are employed to deliver more precise *** phase follows feature engineering techniques,including diverse feature ***,we used the graph mining method for feature optimization and AdaBoost for *** tested our proposed HVAA model on two benchmark *** testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)*** second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event *** proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.

关键词： AdaBoost classification deep features mining graph mining human detection human verification

A clique-superposition model for social networks

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Science China(Information Sciences) 2013年第5期56卷 172-190页

作者： YAN Fei CAI ShaoWei ZHANG Ming LIU GuoJun DENG ZhiHong School of Electronics Engineering and Computer Science Peking University Administrative Efficiency Construction Office of Chengdu Municipal People's Government

How does a social network evolve? Sociologists have studied this question for many *** to some famous sociologists,social links are driven by social *** who affiliate with the shared intersections tend to become interpersonally linked and form a *** the social network,an actor cluster could be a clique or a group of several smaller-sized *** we can conclude that a social network is composed of superposed cliques of different ***,sociologists did not verify the theory in large scale data due to lack of computing *** by this challenge,incorporated with the theory,we utilize data mining technologies to study the evolution patterns of large scale social networks in real ***,we propose a novel Clique-superposition generative model,which generates undirected weighted *** extensive experiments,we demonstrate that our model can generate networks with static and time evolving patterns observed not only in earlier literature but also in our work.

关键词： social networks graph mining Clique-superposition graph generator KEWLS

Prerequisite Relations among Knowledge Units:A Case Study of Computer Science Domain

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 639-652页

作者： Fatema Nafa Amal Babour Austin Melton Department of Computer Science Salem State UniversitySalemMA 01970USA Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Kent State UniversityKentOH 44240USA

The importance of prerequisites for education has recently become a promising research *** work proposes a statistical model for measuring dependencies in learning resources between knowledge *** are expected to present knowledge units in a semantically well-organized manner to facilitate students’understanding of the *** proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge *** help understand the complexity of the inner concepts themselves,WordNet is included as an external knowledge base in *** goal is to develop a model that will enable instructors to evaluate whether or not a learning regime has hidden relationships which might hinder students’ability to understand the *** evaluation,employing three textbooks,shows that the proposed model succeeds in discovering hidden relationships among knowledge units in learning resources and in exposing the knowledge gaps in some knowledge units.

关键词： Knowledge graph text mining knowledge unit graph mining