Keyword Search over Distributed Graphs with Compressed Signature

Yuan, Ye; Lian, Xiang; Chen, Lei; Yu, Jeffery Xu; Wang, Guoren; Sun, Yongjiao

doi:10.1109/tkde.2017.2656079

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…Generally, graph keyword search [183,204,206,208] aims to find a tree or a subgraph, which contains a set of query keywords, from a large graph G. Earlier studies often output a tree structure. In [20] Recently, some solutions have output subgraphs.…”

Section: Graph Abstractearchmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

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With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works.In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze

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Section: Graph Abstractearchmentioning

confidence: 99%

A survey of community search over big graphs

et al. 2019

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“…Moreover, in PRAO, the value of weather condition of an edge changes over time, for example, an edge might not satisfy weather condition at 9:00am, but it may be safe to be taken at 9:15am. Similarly, Yuan et al [37] considered certain data sets over the distributed environment, whereas our PRAO problem is considered over a centralized machine and it involves the probabilistic weather condition (e.g., the temperature is 50 • at 8am with probability 0.6). "…”

Section: Related Workmentioning

confidence: 99%

“…Complexity AnalysisIn this subsection, we discuss the time complexity of the PRAO-QP algorithm in Algorithm 2. The major time cost of Algorithm 2 consists of two portions: index traversal (lines 1-28) and candidate path expansion (lines[29][30][31][32][33][34][35][36][37][38][39][40][41].Without loss of generality, assume that the connection graph (mentioned in Section 5.3) for the i-th level of the (a) Illustration of road network with its corresponding graph representation. (b) The R * -tree representation of road network in (a).…”

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confidence: 99%

“…Finally, we will use the heap H to further expand (partial) paths in it, and obtain complete paths from src to dst (lines[29][30][31][32][33][34][35][36][37][38][39][40]. Specifically, each time we obtain an entry (key, Path) from heap H with the minimum key (i.e., time lower bound; lines[29][30].…”

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confidence: 99%

“…Then, we try to expand the path Path by increasing its length by 1, and obtain a path Path (lines 33). If path Path can pass the weather-based, keyword-based, and traveling time pruning and reaches destination dst, we will add it to the candidate set S cand and update threshold λ (lines[34][35][36][37][38]; otherwise…”

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confidence: 99%

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