Big Graph Analyses: From Queries to Dependencies and Association Rules

Fan, Wenfei; Hu, Chunming

doi:10.1007/s41019-016-0025-x

Cited by 22 publications

(5 citation statements)

References 83 publications

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“…Community detection [28,34,35] is always a hotspot research in graph data mining. At the initial stage, researchers focused on gathering users with close topological distance to form a community.…”

Section: Related Workmentioning

confidence: 99%

Node Embedding Research Over Temporal Graph

Wu¹,

Wu²,

Yuan³

et al. 2021

International Journal of Software and Informatics

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Compared with conventional graph data analysis methods, the graph embedding algorithm provides a new graph data analysis strategy. It aims to encode graph nodes into vectors to mine or analyze graph data more effectively using neural networks related technologies. Some classic tasks have been improved significantly by graph embedding methods, such as node classification, link prediction, and traffic flow prediction. Although substantial breakthroughs have been made by former researchers in graph embedding, the nodes embedding problem over temporal graph have been seldom studied. In this work, we propose an Adaptive Temporal Graph EmBebding (ATGEB), attempting to encode temporal graph nodes into vectors by combining previous research and the information propagation characteristics. First, an adaptive cluster method is proposed by solving the situation that nodes active frequency varies types of graph. Then, a new node walk strategy is designed in order to store the time sequence between nodes, and also the walking list will be stored in a bidirectional multi-tree in the walking process to get complete walking lists quickly. Last, based on the basic walking characteristics and graph topology, an important node sampling strategy is proposed to train the satisfied neural network as soon as possible. Sufficient experiments demonstrate that the proposed method surpasses existing embedding methods in terms of node clustering, reachability prediction, and node classification in temporal graphs.

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Section: Related Workmentioning

confidence: 99%

Node Embedding Research Over Temporal Graph

Wu¹,

Wu²,

Yuan³

et al. 2021

International Journal of Software and Informatics

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show abstract

“…There have been fast growing interests in the study of massive data sets. The research has included the study of structures of massive data and data queries (e.g., [11]), parallel and distributed processing of massive data (e.g., [20]), and preprocessing of massive data (e.g., [10]). The research has been driven directly by practical applications in massive data processing, and is essentially heuristic-based.…”

Section: Motivationsmentioning

confidence: 99%

Linear-Time Parameterized Algorithms with Limited Local Resources

Chen¹,

Guo²,

Huang³

2020

Preprint

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We propose a new (theoretical) computational model for the study of massive data processing with limited computational resources. Our model measures the complexity of reading the very large data sets in terms of the data size N and analyzes the computational cost in terms of a parameter k that characterizes the computational power provided by limited local computing resources. We develop new algorithmic techniques that implement algorithms for solving well-known computational problems on the proposed model. In particular, we present an algorithm that finds a k-matching in a general unweighted graph in time O(N + k 2.5 ) and an algorithm that constructs a maximum weighted k-matching in a general weighted graph in time O(N + k 3 log k). Both algorithms have their space complexity bounded by O(k 2 ).

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“…A user submits a job encompass of a map function and a reduce function that are consequently altered into map and decrease tasks, such tasks are scheduled [5] on slot hosted by contribute nodes in the cluster. The author present network theory-based method to pull out the topological and dynamical network properties of clusters used in the era of big data [18,20]. The random networks and free-scale networks are best examples of such dynamic networks and such kind of networks are ranked on the beginning of statistical parameters, namely standard deviation, mean and variance.…”

Section: Priority Based Classical Data Encapsulated Scheduling For Nementioning

confidence: 99%

Priority Based Classical Data Encapsulated Scheduling For New Era in Computing Environment

Gavaskar¹,

Rajkumar²,

Sudheer³

2019

IJITEE

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In computational environment the data processing and data transactions are major issue, to overcome this problem of processing and reduce the band width in distributed models. The abstract mentioned here clearly state the novelty of the work regarding to data processing and data utilization. This paper shows that the data provided between nodes that are involved in clusters and that is useful to the data utilization schematic in all kinds of clusters. The proposed approach arranges data into data tables and distributes them transversely nodes in a cluster. It recommends jobs to be processed by the cluster then transfers packaged and encapsulated data into nodes to process the data in parallel. This technique employs an incremental data scheduled computation way that keep away from the costly enumeration of data pattern matching necessary by cluster methods. It improves the data locality by forwarding data to the job supporting cluster. This abstraction is enthused by the data processing and data reduce primitives presented in the various job processing environment and many other functional applications used in data grid, data intensive approaches. The Architectural interface is provided in between clusters and is used to achieve high performance on large clusters of commodity PCs. This approach reduces the data request and its processing when it is signed into data clusters. The proposed approach arranges data into data tables and distributes them across nodes in a cluster. It recommend jobs to be processed by the cluster then transfers packaged and encapsulated data into nodes to progression the data in parallel. It improves the data locality by forwarding data to the job supporting cluster. The proposed approach in this paper is helpful where number of nodes involve in cluster is always increasing due to the high end computations that are involved in distributed environment. ), otherwise Constraint satisfactions Algorithm:Step -1: initialize variable of job table J ET = 0 J p =0; J ET  J p ( J ET defines J p ) Step-2: Tolerate data set with D t ( d i ,d p , d tv )

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Big Graph Analyses: From Queries to Dependencies and Association Rules

Cited by 22 publications

References 83 publications

Node Embedding Research Over Temporal Graph

Node Embedding Research Over Temporal Graph

Linear-Time Parameterized Algorithms with Limited Local Resources

Priority Based Classical Data Encapsulated Scheduling For New Era in Computing Environment

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