Mining Recurrent Patterns in a Dynamic Attributed Graph

Zhi, Cheng; Flouvat, Frédéric; Selmaoui-Folcher, Nazha

doi:10.1007/978-3-319-57529-2_49

Cited by 13 publications

(19 citation statements)

References 12 publications

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“…As an example, mining-trend motifs help to identify a group of nodes or agents that show similar increasing or decreasing trends over time [163]. More complex trends such as recurrent trends are identified in a set of nodes over a sequence of time intervals using algorithms, such as RPMiner [164]. The identified patterns in attributes and states may entail changes in the network topological structures and agnets communication, which are known as triggering patterns [165].…”

Section: Pattern Recognitionmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

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Section: Pattern Recognitionmentioning

confidence: 99%

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

Nezamoddini

Gholami

2022

Smart Cities

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“…A user can utilize these interestingness measures to more precisely express his preferences to select patterns, which can improve the efficiency of the algorithm (because constraints can help to reduce the search space). Cheng et al (2017) generalized the concept of cohesive co-evolution pattern by proposing a new type of patterns called recurrent patterns. These patterns capture how attribute values changed for a set of vertices over a sequence of time intervals.…”

Section: Mining Cohesive Co-evolution Patternsmentioning

confidence: 99%

“…Moreover, additional constraints may be integrated into algorithms to select more interesting patterns and reduce the search space. Discover patterns in more complex data . A trend in recent years has been to consider more complex data types such as attributed graphs (Cheng et al, 2017; Desmier et al, 2012; Desmier, Plantevit, Robardet, & Boulicaut, 2013; Fournier‐Viger, Cheng, Cheng, Lin, & Selmaoui‐Folcher, 2019e), streams (Nishioka & Scherp, 2018; Ray et al, 2014), and graphs with uncertainty (Leung & Cuzzocrea, 2015). The reason is that complex data are found in many applications.…”

Section: Research Opportunitiesmentioning

confidence: 99%

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A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

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Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.

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“…In the last decades, more and more data has been collected and stored in databases. In that context, graphs are playing an increasingly important role because they can model complex structures such as chemical molecules, social networks, computer networks, and links between web pages [16,6,5,11,7]. To discover interesting knowledge in graphs, algorithms have been proposed to mine various types of patterns such as frequent subgraphs, trees, paths, periodic patterns and motifs [10,16].…”

Section: Introductionmentioning

confidence: 99%

Mining Attribute Evolution Rules in Dynamic Attributed Graphs

Fournier‐Viger

Lin

et al. 2020

Big Data Analytics and Knowledge Discovery

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A dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and the set of vertices in a pattern to be fixed, while others consider that a single vertex may influence its neighbors. As a result, these algorithms are unable to find complex patterns that show the influence of multiple vertices on many other vertices in terms of several attributes and different trends. This paper addresses this issue by proposing to discover a novel type of patterns called attribute evolution rules (AER). These rules indicate how changes of attribute values of multiple vertices may influence those of others with a high confidence. An efficient algorithm named AER-Miner is proposed to find these rules. Experiments on real data show AER-Miner is efficient and that AERs can provide interesting insights about dynamic attributed graphs.

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Mining Recurrent Patterns in a Dynamic Attributed Graph

Cited by 13 publications

References 12 publications

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

A survey of pattern mining in dynamic graphs

Mining Attribute Evolution Rules in Dynamic Attributed Graphs

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