Distributed aggregation-based attributed graph summarization for summary-based approximate attributed graph queries

Yang, Shang; Yang, Zhipeng; Chen, Xiaona; Zhao, Jingpeng; Ma, Yinglong

doi:10.1016/j.eswa.2021.114921

Cited by 1 publication

(2 citation statements)

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“…In order to completely illustrate the approaches, Liu et al [19] summed up the graphbased summarization methods in four basis techniques relying on different objectives. Aggregation based [20][21][22][23][24], bit compression based [25], simplification based [26,27], and influence based [28] are a suite of core techniques that have been used in many studies. Aggregation based is one of the most prominent techniques that grouped nodes or edges into super nodes/edges using its optimization function which can be divided into node-grouping and edge-grouping methods.…”

Section: Graph-based Summarizationmentioning

confidence: 99%

“…One of the edgegrouping methods is the graph dedensification which enhances query processing by compressing dense high-degree vertices in a graph [24]. Recently [23], a distributed aggregation-based graph summarization for large-scale approximate attributed graph queries was proposed to semantically produce a graph summary for improving computational costs and maintaining query accuracy. A bit compression in graph-based summarization focuses on reducing number of bits in the original graph, where reporting core structural pattern along with some original components in the modeled graph.…”

Section: Graph-based Summarizationmentioning

confidence: 99%

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Using graph evolutionary to retrieve related tweets

Yovithaya

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Due to its popularity and daily active users, social media has become powerful and influential in the last decade. With the nature of a micro-blogging platform, instant messages and the latest short posts are sent throughout the network on Twitter. Therefore, most users utilize Twitter to update breaking news or the latest events. Since a huge volume of tweet messages have been published on Twitter, event evolution has also rapidly developed into related events within similar topics. In this study, we present a novel method to retrieve tweets that relate to a given query term. Not only perfectly matched tweets, but more related tweets will be retrieved. The collected tweet data are processed and constructed as an original network. With the benefits of social network analysis, a simplification-based summarization approach is applied to ignore information that has less importance while preserving significant information in the network based on centrality measurement and clustering coefficient. Using the evolutionary of graph-based representation extends the relationship diffusion to assist related information retrieval. Experiments were performed using Thai news datasets and the framework performance was evaluated by precision, recall, and f-score. The experimental results show that our framework outperformed the baseline methods which derived a similarity score based on the word-embedded vector to find relevant documents.

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