Incremental Frequent Subgraph Mining on Large Evolving Graphs

Abdelhamid, Ehab; Canim, Mustafa; Sadoghi, Mohammad; Bhattacharjee, Bishwaranjan; Chang, Yuan-Chi; Kalnis, Panos

doi:10.1109/tkde.2017.2743075

Cited by 45 publications

(20 citation statements)

References 43 publications

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“…Based on these concepts, three types of dynamic graphs can be identified: (a) dynamic graphs with only topological evolution (Abdelhamid et al, 2018;Berlingerio et al, 2009;Borgwardt et al, 2006;Halder et al, 2016;Lahiri & Bergerwolf, 2009;Leung, Lim, Lo, & Weng, 2010;Ozaki & Etoh, 2011), (b) dynamic graphs with only label evolution (Jin et al, 2007), and (c) dynamic graphs with both topological and label evolution (Ahmed & Karypis, 2015a;Ahmed & Karypis, 2015b;Scharwachter, Muller, Donges, Hassani, & Seidl, 2016;Vaculik, 2015;Wackersreuther et al, 2010). Studies on the third type of dynamic graphs mostly focus on a type of graph called dynamic attributed graphs, which will be discussed in the following subsection.…”

Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

“…Although only vertices are labeled in this example, edge labels can also be considered. Note that besides these main types of graphs, some authors also use the terms evolving graph or streaming graph to refer to a graph that is continuously updated with new snapshots (Abdelhamid et al, 2018;Nishioka & Scherp, 2018;Ray, Holder, & Choudhury, 2014). Mining patterns in a streaming graph is more challenging as results must often be updated in real-time as new data arrives, and if the stream is infinite, data can only be read once.…”

Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

“…Wackerseuther et al applied their approach to find interesting patterns in a single graph representing protein-protein interactions from yeast, where gene expression levels vary over time (Wackersreuther et al, 2010). Contrary to the above two algorithms, which focus on finding frequent subgraphs representing dynamic behaviors, Abdelhamid et al (2018) proposed to identify subgraphs that are frequent in each snapshot of a dynamic graph. To perform this task, a simple solution is to apply a traditional FSM algorithm to each snapshot and then to combine the results.…”

Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

“…Several studies have been done on mining patterns in a data stream of graphs (Nishioka & Scherp, 2018), also sometimes called streaming graphs or evolving graphs (Abdelhamid et al, 2018). A stream is an infinite sequence of graphs arriving at high speed.…”

Section: Extensionsmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

Section: A Taxonomy Of Dynamic Graphsmentioning

confidence: 99%

Section: Extensionsmentioning

confidence: 99%

See 2 more Smart Citations

A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

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“…IncGM+ is an incremental solution that uses a fringe of selected subgraphs and introduces a number of novel ideas that collectively result in superior performance. A long version of this paper appears in [4].…”

Section: Introductionmentioning

confidence: 99%

Incremental Frequent Subgraph Mining on Large Evolving Graphs

Abdelhamid

Canim

Sadoghi

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

Self Cite

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Frequent subgraph mining is a core graph operation used in many domains. Most existing techniques target static graphs. However, modern applications utilize large evolving graphs. Mining these graphs using existing techniques is infeasible because of the high computational cost. We propose IncGM+, a fast incremental approach for frequent subgraph mining on large evolving graphs. We adapt the notion of "fringe" to the graph context, that is, the set of subgraphs on the border between frequent and infrequent subgraphs. IncGM+ maintains fringe subgraphs and exploits them to prune the search space. To boost efficiency, IncGM+ stores a number of selected embeddings to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Our results confirm that IncGM+ outperforms existing methods, scales to larger graphs and consumes less memory.

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Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

Vargas-Solar¹,

Zechinelli-Martini²,

Espinosa-Oviedo³

2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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We argue for the need of a new generation of data science solutions that can democratize recent advances in data engineering and artificial intelligence for non-technical users from various disciplines, enabling them to unlock the full potential of these solutions. To do so, we adopt an approach whereby computational creativity and conversational computing are combined to guide non-specialists intuitively to explore and extract knowledge from data collections. The paper introduces MATILDA, a creativity-based data science design platform, showing how it can support the design process of data science pipelines guided by human and computational creativity.

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Incremental Frequent Subgraph Mining on Large Evolving Graphs

Cited by 45 publications

References 43 publications

A survey of pattern mining in dynamic graphs

A survey of pattern mining in dynamic graphs

Incremental Frequent Subgraph Mining on Large Evolving Graphs

Enacting Data Science Pipelines for Exploring Graphs: From Libraries to Studios

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