.png){kind=icon}
Return
Mining significant trend sequences in dynamic attributed graphs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2019.06.005.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Discovering patterns in graphs has many applications such as social network, biological and chemistry data analysis. Although many algorithms were proposed to identify interesting patterns in graphs, most of them consider simple types of graphs such as static graphs or graphs having a single attribute per vertex. Recently, studies have considered discovering frequent patterns in dynamic attributed graphs. These graphs can represent not only relationships between entities but also how they evolve over time, and describe entities with multiple attributes. Algorithms for mining frequent patterns in dynamic attributed graphs select patterns based on their occurrence frequency. But a major drawback of this approach is that many frequent patterns may contain entities that are weakly correlated. Thus, numerous frequent but spurious patterns may be shown to the user. To allows discovering strongly correlated patterns in dynamic attributed graphs, this paper proposes a novel significance measure named Sequence Virtual Growth Rate. It allows evaluating if a pattern represents entities that are correlated in terms of their proximity in a graph over time. Based on this measure a novel type of graph patterns is defined called Significant Trend Sequence. To efficiently mine these patterns, two algorithms named TSeqMiner(dfs-bfs), and TSeqMiner(dfs-bfs) are proposed. They rely on a novel upper bound and pruning strategy to reduce the search space. Experimental results show that the proposed algorithms are efficient and can identify interesting patterns in real-world social network and flight data. (C) 2019 Published by Elsevier B.V.
Keywords:
Graph mining
Dynamic attributed graph
Significance measure
Sequence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
Organization
View more organizations
Cited Papers
Creation of three-dimensional liver tissue models from experimental images for systems medicine.png)
IF0
PREAI
ShareSaveAnaerobic and aerobic components during arm-crank exercise in sprint and middle-distance swimmers
PREAI
ShareSaveShareSaveShareSaveModeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid AutomataSENSORS
OAAI
SENSORSIF3.5
OAAIShareSaveShareSave View more