2015
DOI: 10.1007/s10115-015-0847-2
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Discovery of “comet” communities in temporal and labeled graphs Com $$^2$$ 2  

Abstract: While the analysis of unlabeled networks has been studied extensively in the past, finding patterns in different kinds of labeled graphs is still an open challenge. Given a large edge-labeled network, e.g., a time-evolving network, how can we find interesting patterns? We propose Com 2 , a novel, fast and incremental tensor analysis approach which can discover communities appearing over subsets of the labels. The method is (a) scalable, being linear on the input size, (b) general, (c) needs no user-defined par… Show more

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Cited by 6 publications
(3 citation statements)
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“…In database and data mining, there is a large body of literature on finding patterns in temporal graphs [5,25,29,38,46,47,52]. Hu et al [32] studied the problem of computing temporal join queries efficiently; the problem of finding durable triangles is a special case of the problem they studied with self-joins.…”
Section: Related Workmentioning
confidence: 99%
“…In database and data mining, there is a large body of literature on finding patterns in temporal graphs [5,25,29,38,46,47,52]. Hu et al [32] studied the problem of computing temporal join queries efficiently; the problem of finding durable triangles is a special case of the problem they studied with self-joins.…”
Section: Related Workmentioning
confidence: 99%
“…Sun et al ( 2007) introduce a block summary approach for dynamic graphs, extended to multiple dimensions or contexts by Jiang et al (2016). Araujo et al (2014b) also propose a block summary approach for dynamic graphs, which they later extend to multiple dimensions or contexts as represented by qualitative labels on the edges (Araujo et al 2016).…”
Section: Grouping Nodes Into Blocks In Dynamic Graphsmentioning
confidence: 99%
“…In this paper, we are interested in a more specific problem, that of identifying the densest subgraph over time, which in some sense can be viewed as a special type of a tightly-knit evolving community. Various approaches have been proposed for discovering communities in time-evolving graphs including incremental tensor analysis (e.g., [90]).…”
Section: Related Workmentioning
confidence: 99%